{"schema_version":"onlylabs.public_analysis_evidence.v1","title":"Cerebras analysis evidence pack","description":"Public onlylabs evidence pack for cited agent analysis: captured pages, ranked public signals, and stored web-search provenance used by the background analysis workflow.","url":"https://onlylabs.fyi/analysis/cerebras","json_url":"https://onlylabs.fyi/analysis/cerebras/evidence.json","generated_at":"2026-06-27T22:36:19.335Z","org":{"slug":"cerebras","name":"Cerebras","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/cerebras"},"analysis":{"url":"https://onlylabs.fyi/analysis/cerebras","json_url":"https://onlylabs.fyi/analysis/cerebras/analysis.json","generated_at":"2026-06-27T18:49:43.968+00:00"},"workflow":{"version":"onlylabs-deepagents-analysis-v3","provider":"deepseek","model":"deepseek-v4-pro","agent":"deepagents","public_pack_mode":"local-pages-and-events","live_web_fetches":false,"note":"Public evidence exports do not trigger live Exa calls; stored Exa provenance is included when analysis metadata contains it."},"stats":{"pages":28,"events":140,"web":0,"evidence":88,"signal_desks":{"hiring":47,"forks":0,"releases":1,"talking":12,"repos":0},"data_radar_lanes":null,"data_radar_matches":null,"stored_analysis_evidence":93,"stored_analysis_web":5,"stored_analysis_signal_desks":{"forks":0,"repos":0,"hiring":47,"talking":12,"releases":1},"stored_analysis_data_radar_lanes":null,"stored_analysis_data_radar_matches":null},"stored_web_provenance":{"queries":["\"Cerebras\" frontier AI lab recent model release research hiring GitHub Hugging Face","\"Cerebras\" AI lab what they are building talking about hiring releasing forking"],"request_ids":["4cc9cdb6b8b472d5c1fd7a6b44551a7a","4c5a893bc62b5f57ccaf888d36f83678"],"skipped":null},"evidence":[{"ref":"P1","kind":"page","title":"Epcc Edinburgh Why Cerebras Professor Mark Parsons June 2021","date":"2026-06-27T16:01:09.633248+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/epcc-edinburgh-why-cerebras-professor-mark-parsons-june-2021","signal_url":null,"signal_json_url":null,"text":"EPCC Edinburgh (Why Cerebras): Professor Mark Parsons June 2021 - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nJul 02 2021\nEPCC Edinburgh (Why Cerebras): Professor Mark Parsons June 2021 - Cerebras\nRebecca Lewington \n\nOur Vice President of Product Andy Hock joins Mark Parsons of EPCC and Professor at University of Edinburgh to discuss the conundrum between supercomputing and data science. The data science side of things often runs out of horsepower for demanding users, and this is what we are solving with Cerebras CS-1. Mark says, ‘this is the first time to reach the terabyte regime.\n\nFollow\n\nGet Updates\nNewsletter Signup \n\nCompany\nAbout Us \nCareers \nContact Us \nInvestor Relations \nWebsite Terms of Use \nPrivacy Policy \nCookie Policy \nOther Terms & Policies \nService Status \nTrust Center \n\nNews\nNewsroom \nIn the News \nPress kit \n\nInsights\nCustomer Spotlight \nBlog \nPublications \nWhitepapers \n\nPerformance comparisons are based on third-party benchmarking or internal testing. Observed inference speed improvements versus GPU-based systems may vary depending on workload, configuration, date and models being tested.\ninfo@cerebras.ai \n1237 E. Arques Ave
\nSunnyvale, CA 94085\n\n© 2026 Cerebras. \nAll rights reserved."},{"ref":"P2","kind":"page","title":"Cerebras Systems Overview Andy Hock Vp Product Management","date":"2026-06-27T16:01:09.367995+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/cerebras-systems-overview-andy-hock-vp-product-management","signal_url":null,"signal_json_url":null,"text":"Cerebras Systems Overview: Andy Hock, VP Product Management - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nJul 02 2021\nCerebras Systems Overview: Andy Hock, VP Product Management - Cerebras\nRebecca Lewington \n\nCerebras was founded because we saw an opportunity to transform the computing landscape by building the right computer system to radically accelerate artificial intelligence. We built our platforms for researchers to develop new neural network architectures and new algorithms that are not efficient or practical on existing machines.\n\nFollow\n\nGet Updates\nNewsletter Signup \n\nCompany\nAbout Us \nCareers \nContact Us \nInvestor Relations \nWebsite Terms of Use \nPrivacy Policy \nCookie Policy \nOther Terms & Policies \nService Status \nTrust Center \n\nNews\nNewsroom \nIn the News \nPress kit \n\nInsights\nCustomer Spotlight \nBlog \nPublications \nWhitepapers \n\nPerformance comparisons are based on third-party benchmarking or internal testing. Observed inference speed improvements versus GPU-based systems may vary depending on workload, configuration, date and models being tested.\ninfo@cerebras.ai \n1237 E. Arques Ave
\nSunnyvale, CA 94085\n\n© 2026 Cerebras. \nAll rights reserved."},{"ref":"P3","kind":"page","title":"Gsk Dreaming Big With Cerebras Dr Kim Branson","date":"2026-06-27T16:01:09.007069+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/gsk-dreaming-big-with-cerebras-dr-kim-branson","signal_url":null,"signal_json_url":null,"text":"GSK (Dreaming Big with Cerebras): Dr. Kim Branson - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nJul 07 2021\nGSK (Dreaming Big with Cerebras): Dr. Kim Branson - Cerebras\nRebecca Lewington \n\nDream big with Cerebras! GSK discovered Cerebras when they did a survey and Cerebras caught their attention because ‘they use silicon you can use, and a technological stack geared to what we do.’ Learn about our strategic partnership with GSK. #AI #DataScience #ML #Tensorflow\n\nFollow\n\nGet Updates\nNewsletter Signup \n\nCompany\nAbout Us \nCareers \nContact Us \nInvestor Relations \nWebsite Terms of Use \nPrivacy Policy \nCookie Policy \nOther Terms & Policies \nService Status \nTrust Center \n\nNews\nNewsroom \nIn the News \nPress kit \n\nInsights\nCustomer Spotlight \nBlog \nPublications \nWhitepapers \n\nPerformance comparisons are based on third-party benchmarking or internal testing. Observed inference speed improvements versus GPU-based systems may vary depending on workload, configuration, date and models being tested.\ninfo@cerebras.ai \n1237 E. Arques Ave
\nSunnyvale, CA 94085\n\n© 2026 Cerebras. \nAll rights reserved."},{"ref":"P4","kind":"page","title":"Andy Hock Talk Epcc Webinar June 2021","date":"2026-06-27T16:01:08.86022+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/andy-hock-talk-epcc-webinar-june-2021","signal_url":null,"signal_json_url":null,"text":"Andy Hock Talk: EPCC Webinar June 2021 - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nJul 02 2021\nAndy Hock Talk: EPCC Webinar June 2021 - Cerebras\nRebecca Lewington \n\nLearn more about the Cerebras CS-1 as Andy Hock discusses large scale #AI for data-driven innovation. In this video, he demonstrates training #BERT for a web #NLP application. Test for yourself and sign up for early testing and early access at https://bit.ly_EPCC .\n\nFollow\n\nGet Updates\nNewsletter Signup \n\nCompany\nAbout Us \nCareers \nContact Us \nInvestor Relations \nWebsite Terms of Use \nPrivacy Policy \nCookie Policy \nOther Terms & Policies \nService Status \nTrust Center \n\nNews\nNewsroom \nIn the News \nPress kit \n\nInsights\nCustomer Spotlight \nBlog \nPublications \nWhitepapers \n\nPerformance comparisons are based on third-party benchmarking or internal testing. Observed inference speed improvements versus GPU-based systems may vary depending on workload, configuration, date and models being tested.\ninfo@cerebras.ai \n1237 E. Arques Ave
\nSunnyvale, CA 94085\n\n© 2026 Cerebras. \nAll rights reserved."},{"ref":"P5","kind":"page","title":"Epcc Edinburgh Projects On Cs 1 Professor Mark Parsons June 2021","date":"2026-06-27T16:01:08.82039+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/epcc-edinburgh-projects-on-cs-1-professor-mark-parsons-june-2021","signal_url":null,"signal_json_url":null,"text":"EPCC Edinburgh (Projects on CS-1): Professor Mark Parsons June 2021 - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nJul 02 2021\nEPCC Edinburgh (Projects on CS-1): Professor Mark Parsons June 2021 - Cerebras\nRebecca Lewington \n\nEPCC has been using the Cerebras CS-1 for about a month and has already made tremendous progress. One of the teams is looking at the GCN/LSTM/Conv1D networks that come with the software framework they have on the Cerebras CS-1. EPCC is also collaborating with a group of PhD students who are part of the biomedical AI PhD program. They are taking the projects that have been running to the MGPUs and putting them onto the Cerebras system. Learn more and sign up for early testing and early access at https://bit.ly_EPCC .\n\nFollow\n\nGet Updates\nNewsletter Signup \n\nCompany\nAbout Us \nCareers \nContact Us \nInvestor Relations \nWebsite Terms of Use \nPrivacy Policy \nCookie Policy \nOther Terms & Policies \nService Status \nTrust Center \n\nNews\nNewsroom \nIn the News \nPress kit \n\nInsights\nCustomer Spotlight \nBlog \nPublications \nWhitepapers \n\nPerformance comparisons are based on third-party benchmarking or internal testing. Observed inference speed improvements versus GPU-based systems may vary depending on workload, configuration, date and models being tested.\ninfo@cerebras.ai \n1237 E. Arques Ave
\nSunnyvale, CA 94085\n\n© 2026 Cerebras. \nAll rights reserved."},{"ref":"P6","kind":"page","title":"Multi Billion Parameter Model Training Made Easy With Csoft R1 3","date":"2026-06-27T16:01:08.340871+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/multi-billion-parameter-model-training-made-easy-with-csoft-r1-3","signal_url":null,"signal_json_url":null,"text":"Multi-Billion-Parameter Model Training Made Easy with CSoft R1.3 - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nJun 01 2022\nMulti-Billion-Parameter Model Training Made Easy with CSoft R1.3 - Cerebras\nNatalia Vassilieva \n\nNatalia Vassilieva, Director of Product | June 1, 2022 \n\nWe’re excited to announce the release of Cerebras Software Platform (CSoft) version 1.3. This release enables training and fine-tuning of GPT-J 6B parameters model, further expands PyTorch support and delivers new optimizations to make training of smaller transformers (up to one billion parameters, like the original Transformer and BERT models) even faster.\n\nGPT-J Made Easy with Cerebras’ Weight Streaming Technology\n\nJust four years ago, state-of-the-art natural language processing models (NLP) had 100 million parameters and we thought that was massive. Now, with CSoft 1.3 it is now possible to train autoregressive language models with billions of parameters on a single CS-2 system using our groundbreaking weight streaming execution mode. Easily!\n\nProbably the most exciting use case for this capability is continuous pre-training and fine-tuning of GPT-J. GPT-J is an open autoregressive language model with 6B parameters trained and released by EleutherAI . Availability of the trained weights for this model and lower serving cost compared to larger models such as OpenAI’s GPT-3 , have made it very popular in the NLP community. GPT-J is considered as a competitive open alternative to GPT-3 and the generic version has demonstrated reasonably good results on a number of natural language tasks without any further training, in zero-shot setting. (Zero-shot learning is a seemingly magical method where a model learns how to make predictions about things it hasn’t seen before.)\n\nHowever, to use the full power of this model for a domain-specific task, it is best to adapt it to the task at hand via fine-tuning. If your task is in healthcare, a model that doesn’t know the difference between the outcome of a disease and the outcome of a tennis match isn’t very useful!\n\nFine-tuning refers to a process of continuous training from a pre-existing checkpoint with domain-"},{"ref":"P7","kind":"page","title":"Variable Sequence Length Training For Long Context Large Language Models","date":"2026-06-27T16:01:08.316205+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/variable-sequence-length-training-for-long-context-large-language-models","signal_url":null,"signal_json_url":null,"text":"Variable Sequence Length Training for Long-Context Large Language Models - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nJul 22 2023\nVariable Sequence Length Training for Long-Context Large Language Models - Cerebras\nSiyun Li \n\nAbstract\n\nTraining large language models with long sequence lengths is prohibitive in practice and expensive due to long training times. In this article, we introduce you to a simple-to-use training method called Variable Sequence Length (VSL), which can reduce wall-clock times for training large language models with long sequence lengths capabilities without any changes in model architecture and training hyperparameters. Training a GPT model using VSL (2k sequence length followed by 8k sequence length) uses 29% fewer FLOPs over training with 8k sequence length all the way through while achieving the same model performance.\n\nIntroduction\n\nThe recent popularity of next-generation AI assistants powered by large language models (LLMs), like Chat-GPT and Claude , has increased the demand for long-context capabilities, especially for applications like long multi-turn conversations, summarizing documents, and code completion, which require the model to understand long-range dependencies. Figure 1 shows the rapid growth in sequence lengths supported by some prevalent foundation models.\n\nAn inherent challenge in scaling to long sequence lengths is the quadratic scaling of memory and compute for self-attention, which limits training most large GPT models to sequence lengths of 2k. Recent techniques like Flash Attention [1] and Memory-Efficient Attention [2] have proposed drastically reducing memory overheads by splitting the attention computation using smaller sub-blocks. Other methods, such as ALiBi [3], propose incorporating contextual dependencies during model training to enable scaling of sequence lengths during inference. These techniques, however, do not fully address the quadratic compute as we scale to longer sequences. Sparse attention methods such as Longformer [4] and Performer [5] reduce computation and memory costs by approximating the full attention pattern using frameworks such as sliding windows"},{"ref":"P8","kind":"page","title":"Accelerating Large Gpt Training With Sparse Pre Training And Dense Fine Tuning","date":"2026-06-27T16:01:08.061501+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/accelerating-large-gpt-training-with-sparse-pre-training-and-dense-fine-tuning","signal_url":null,"signal_json_url":null,"text":"Accelerating Large GPT Training with Sparse Pre-Training and Dense Fine-Tuning [Updated] - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nMar 21 2023\nAccelerating Large GPT Training with Sparse Pre-Training and Dense Fine-Tuning [Updated] - Cerebras\nVithursan Thangarasa \n\n[Updated March 2023 with updated results and a link to the new paper.] \n\nAbstract\n\nIn an industry where the exponential growth in the size of large GPT models has resulted in prohibitively high training costs, the ability to reduce the compute to train these models is a fundamental enabler. We believe sparsity is a key to reducing that compute and there are machine learning (ML) techniques to ensure the resulting models have the quality of their dense counterparts. With the Cerebras CS-2 system’s unique ability to run large models easily while accelerating unstructured sparsity, we are starting to explore these ML techniques at a scale that was not practical before. Until now, most published sparsity research has been limited to models 10x smaller than the ones we use.\n\nIn our paper, SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models , presented at the ICLR 2023 Workshop on Sparsity in Neural Networks , we demonstrate it is possible to pre-train large GPT models with high levels of sparsity followed by dense fine-tuning to preserve accuracy on downstream tasks. This is our first sparsity study on training models of full size, done only in a matter of weeks made possible by the Cerebras CS-2.\n\nSpecifically, we start by using simple, static sparsity and evaluate model sizes up to GPT-3 XL with 1.3B parameters. Our initial results show we can pre-train GPT-3 XL with up to 75% unstructured sparsity and 60% fewer training FLOPS on Cerebras CS-2, while using dense fine-tuning to preserve the evaluation metrics on many downstream tasks. To the best of our knowledge, this is the first time a large GPT model has been pre-trained with high sparsity without significant loss in downstream task metrics. These initial findings with static sparsity show the promise of sparse training, and we are motivated to explore more advanced sparse techniques for"},{"ref":"P9","kind":"page","title":"Btlm 3b 8k 7b Performance In A 3 Billion Parameter Model","date":"2026-06-27T16:01:07.781329+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/btlm-3b-8k-7b-performance-in-a-3-billion-parameter-model","signal_url":null,"signal_json_url":null,"text":"BTLM-3B-8K: 7B Performance in a 3 Billion Parameter Model - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nJul 24 2023\nBTLM-3B-8K: 7B Performance in a 3 Billion Parameter Model - Cerebras\nNolan Dey \n\nCerebras and Opentensor are pleased to announce BTLM-3B-8K (Bittensor Language Model), a new state-of-the-art 3 billion parameter open-source language model that achieves breakthrough accuracy across a dozen AI benchmarks. Given that the most popular model on Hugging Face today is 7B, we believe compacting 7B performance to 3B is an important milestone in enabling AI access on mobile and edge devices. Unlike large models like GPT-3 that runs from the cloud, BTLM fits in mobile and edge devices with as little as 3GB of memory, helping democratize AI access to billions of devices worldwide.\n\nBTLM was trained on the newly unveiled Condor Galaxy 1 (CG-1) AI supercomputer, the first public deliverable of the strategic partnership between Cerebras and G42 . We would like to acknowledge the generous support of two G42 companies, who provided assistance in this work. G42 Cloud and IIAI. We would also like to thank our partner Cirrascale , who first introduced Opentensor to Cerebras and provided additional technical support.\n\nBTLM-3B-8K is available on Hugging Face with an Apache 2.0 license for commercial use.\n\nBTLM-3B-8K Highlights:\n\n7B level model performance in a 3B model\nState of the art 3B parameter model\nOptimized for long sequence length inference 8K or more\nFirst model trained on the SlimPajama , the largest fully deduplicated open dataset\nRuns on devices with as little as 3GB of memory when quantized to 4-bit\nApache 2.0 license for commercial use\n\nBTLM was commissioned by the OpenTensor foundation for use on the Bittensor network. Bittensor is a blockchain based network that lets anyone contribute AI models for inference , providing a decentralized alternative to centralized model providers like OpenAI and Google. Bittensor serves over 4,000 AI models with more than 10 trillion model parameters across the network.\n\nLarge Models Don’t Fit on Small Devices\n\nLarge GPT models typically have over 100B parameters, requiring multiple hi"},{"ref":"P10","kind":"page","title":"More Pixels More Context More Insight","date":"2026-06-27T16:01:07.752684+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/more-pixels-more-context-more-insight","signal_url":null,"signal_json_url":null,"text":"More Pixels, More Context, More Insight! - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nJan 27 2023\nMore Pixels, More Context, More Insight! - Cerebras\nJason Wolfe \n\n[Updated April 2023: R1.8 of the Cerebras Software Platform now supports image segmentation on 50 megapixel images, up from 25 megapixels in R1.7.] \n\nDeep learning for computer vision (CV) has progressed rapidly in recent years, with networks able to identify objects in images and generate realistic images based on text input. With the exploding availability of high-quality, high-resolution data [1], researchers must find ways to train deep neural networks on large images and take advantage of their rich contextual information.\n\nThe Cerebras CS-2 system is designed to overcome the limitations of GPUs and allow users to easily and rapidly train large models on high-resolution, 50-megapixel images.\n\nIntroduction\n\nIn 2012, the goal of the ImageNet challenge was to classify small, 224×224 resolution images containing single subjects. Since then, the field of deep learning for computer vision (CV) has progressed rapidly and has tackled increasingly complex challenges.\n\nToday, network architectures are designed to provide pixelwise segmentation, identify and locate up to hundreds of objects in single images, as well as identify small, sparsely distributed objects in large images. Similarly, there has recently been incredible progress in image generation. Diffusion models, while still relatively new, are currently able to generate state-of-the-art quality images and are setting the bar higher for image generation tasks. In particular, diffusion models have demonstrated the ability to generate remarkably realistic images based on text input.\n\nThese increasingly sophisticated tasks often require higher-resolution data, larger models, and more computation. In CV, deep neural networks (DNN) typically consist of many convolutional layers. Each layer must apply the convolution operation to its inputs and save the high-dimensional output activations. As models become deeper, there are more layers which require more computation and more memory. Similarly, as models get bigger,"},{"ref":"P11","kind":"page","title":"To Bfloat Or Not To Bfloat That Is The Question","date":"2026-06-27T16:01:07.411146+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/to-bfloat-or-not-to-bfloat-that-is-the-question","signal_url":null,"signal_json_url":null,"text":"To Bfloat or not to Bfloat? That is the Question! - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nJan 30 2023\nTo Bfloat or not to Bfloat? That is the Question! - Cerebras\nDaria Soboleva \n\nThe bfloat16 data format can shorten the time it takes to train GPT-style deep learning models while preserving the accuracy of the model on downstream tasks.\n\nIn this article, we show how the bfloat16 data format works, how it fits into an automatic mixed precision training for large language models, and share some experimental results.\n\nAutomatic mixed precision\n\nAutomatic mixed precision is a mode that allows training deep learning models with a mix of single precision floating point float32 and half precision floating points such as float16 or bfloat16.\n\nThe benefits of the mixed precision mode are primary lying in performance. It is an optimization technique that allows you to train your networks faster, but without loss in quality. This phenomenon is due to the fact that some layers of the neural networks can be executed without high precision level, such as convolutional or linear layers. They’ve proven to be much faster when executed with float16 or bfloat16. However, other operations, such as reductions often require a higher precision level in order to maintain the same quality results.\n\nThis trade-off of what needs to be casted to half dtype and what should be maintained in a single precision is included in the recipe of “automatic mixed precision algorithm“. In a nutshell, this recipe measures the performance of the network in default precision, then walks through adding castings to run the same network with a mixed precision setting to optimize performance without hurting accuracy.\n\nMixed precision does not require you to specify bfloat16 as a half precision floating point, however, it has shown some benefits over applying float16. Below we are going to discuss bfloat16 in more granular details.\n\nBfloat16 Floating Type\n\nbfloat16 is a 16-bit floating point format for deep learning that’s comprised of one sign bit, eight exponent bits, and seven mantissa bits. This is different from the industry-standard IEEE 16-bit floating point"},{"ref":"P12","kind":"page","title":"If Youre Doing Pharma And Life Sciences Research Without Cerebras System Youre Doing It Wrong","date":"2026-06-27T16:01:07.348756+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/if-youre-doing-pharma-and-life-sciences-research-without-cerebras-system-youre-doing-it-wrong","signal_url":null,"signal_json_url":null,"text":"If You're Doing Pharma and Life Sciences AI Research Without a Cerebras System, You're Doing it Wrong - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nJan 14 2022\nIf You're Doing Pharma and Life Sciences AI Research Without a Cerebras System, You're Doing it Wrong - Cerebras\nRebecca Lewington \n\nRebecca Lewington, Technology Evangelist | March 14, 2022 \n\nAI has the potential to transform the speed, sophistication and safety of drug discovery, yielding better medicines and vaccines. But to be successful, our customers need to train complex models, using huge datasets, very quickly. Training times measured in weeks just won’t do. The researchers need results in hours so they can run many experiments to test their hypotheses.\n\nWhich is where Cerebras comes in. We make the world’s fastest AI accelerator , removing roadblocks to biomedical research, drug discovery and data-driven healthcare. At Cerebras, we’re helping to solve big problems at a who’s who of leading institutions.\n\nNeed proof? Read on to learn about the important work our systems are doing at a some of our major customers.\n\nGlaxoSmithKline is training complex epigenomic models with a previously prohibitively large dataset, made possible for the first time by Cerebras. In this blog , GSK’s Kim Branson, SVP & Global Head of AI and ML writes: “We were able to train the EBERT model in about 2.5 days, compared to an estimated 24 days with a GPU cluster with 16 nodes. This dramatic reduction in training time makes the new models actually useful in a real-world research environment, which is very exciting.\n\nAnd in the technical paper “ Epigenomic Language Models Powered by Cerebras ”, the GSK authors make it “The training speedup afforded by the Cerebras system enabled us to explore architecture variations, tokenization schemes and hyperparameter settings in a way that would have been prohibitively time and resource intensive on a typical GPU cluster.”\n\nAstraZeneca is iterating and experimenting in real-time by running queries on hundreds of thousands of abstracts and research papers with a Cerebras system. Nick Brown, their Head of AI & Data Science, sai"},{"ref":"P13","kind":"page","title":"When Time Is Money Accelerating Nlp Model Training At A Major Financial Institution","date":"2026-06-27T16:01:07.167214+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/when-time-is-money-accelerating-nlp-model-training-at-a-major-financial-institution","signal_url":null,"signal_json_url":null,"text":"When Time is Money: Accelerating NLP Model Training at a Leading Financial Institution - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nMar 21 2022\nWhen Time is Money: Accelerating NLP Model Training at a Leading Financial Institution - Cerebras\nRebecca Lewington \n\nRebecca Lewington, Technology Evangelist | March 21, 2022 \n\nWe just published a case study which explores a project we conducted with a leading financial services institution to help them overcome a roadblock to using advanced neural network models for a wide range of natural language processing (NLP) tasks.\n\nActually, we were able to do more than “help”. Our CS-2 system delivered the compute performance of more than 120 AI-optimized GPUs. No marginal gains here; that’s a huge leap.\n\nWith that performance, we were able to reduce training time for a complex BERTLARGE model by 15X, compared to a leading 8-GPU server, demonstrate dramatic improvements in model prediction confidence, while almost halving energy consumption.\n\nThose are compelling results in any industry, but especially so in the ultra-competitive world of finance, where even small gains in performance can lead to huge monetary rewards. Which explains why the customer asked to remain anonymous. In this field, it isn’t just time that is proverbially money, but also technology.\n\nThe case study, “ Accelerating NLP Model Training and Enabling Higher Accuracy for Financial Services Applications ” was authored by a trio of Cerebras’ women: machine learning solutions engineers Sanjana Mallya and Cindy Orozco Bohorquez, along with machine learning product director Natalia Vassilieva.\n\nThe exciting thing for the customer is that this is about more than simply training an existing model faster. It’s about giving them the freedom to rapidly experiment to find better models, which they couldn’t explore previously because training those new models took too long to be useful. Data scientists at financial institutions have better things to do with their time than waiting for their models to train themselves.\n\nWhat do I mean by “better”? Large neural language models, such as BERT , are really good at many natural languag"},{"ref":"P14","kind":"page","title":"Celebrating International Womens Day","date":"2026-06-27T16:01:06.997698+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/celebrating-international-womens-day","signal_url":null,"signal_json_url":null,"text":"Celebrating International Women’s Day - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nMar 08 2022\nCelebrating International Women’s Day - Cerebras\nLiz Wu \n\nAt Cerebras Systems, we’re on a mission to revolutionize the artificial intelligence (AI) compute landscape with extraordinary people for global impact! Diverse perspectives, curiosity, and ensuring everyone has the opportunity to contribute are key pillars of our culture.\n\nFor International Women’s Day , we asked a few of our extraordinary women to share what they’re working on and how they are breaking the bias to create a gender equal world.\n\nRebekah Leslie-Hurd is the Director of the Compiler team, working on novel programming languages and compilation challenges for our Wafer-Scale Engine (WSE), the world’s largest and fastest AI processor. This includes domain specific languages for programming the WSE, automatic kernel code generation for ML models, and compiling efficient machine code for each Cerebras architecture. Rebekah’s spent her career designing, reasoning about, and compiling for unique hardware architectures. Working in AI is exciting for her, because it’s an area that is evolving rapidly on every front. As a team leader at Cerebras, Rebekah is dedicated to #breakthebias by creating an environment where every single person is able to do their best work – regardless of gender identity, race, ethnicity, or other differences in background. As she explains, “this starts with hiring individuals who are collaborative and open to feedback and continues with processes that make space for everyone to contribute effectively.” She is always looking for opportunities to improve our organizational systems to ensure that women and other underrepresented groups have a level playing field. “Sometimes this means rethinking convention, which can be scary,” she says, “but ultimately everyone wins.”\n\nSaumya Satish is a Senior Product Manager for AI/ML software , focused on building a world class machine learning user experience to make our CS-2 system the easiest, most streamlined platform to program for cluster scale AI. She has always been interested in AI and its applicati"},{"ref":"P15","kind":"page","title":"Accelerating Drug Discovery Research With New Ai Models A Look At The Astrazeneca Cerebras Collaboration","date":"2026-06-27T16:01:06.882291+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/accelerating-drug-discovery-research-with-new-ai-models-a-look-at-the-astrazeneca-cerebras-collaboration","signal_url":null,"signal_json_url":null,"text":"Customer Blog: Accelerating Drug Discovery with New AI Models - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nApr 26 2021\nCustomer Blog: Accelerating Drug Discovery with New AI Models - Cerebras\nRebecca Lewington \n\nSee the original article at: https://larslynnehansen.medium.com/accelerating-drug-discovery-research-with-new-ai-models-a-look-at-the-astrazeneca-cerebras-b72664d8783 \n\nFollow\n\nGet Updates\nNewsletter Signup \n\nCompany\nAbout Us \nCareers \nContact Us \nInvestor Relations \nWebsite Terms of Use \nPrivacy Policy \nCookie Policy \nOther Terms & Policies \nService Status \nTrust Center \n\nNews\nNewsroom \nIn the News \nPress kit \n\nInsights\nCustomer Spotlight \nBlog \nPublications \nWhitepapers \n\nPerformance comparisons are based on third-party benchmarking or internal testing. Observed inference speed improvements versus GPU-based systems may vary depending on workload, configuration, date and models being tested.\ninfo@cerebras.ai \n1237 E. Arques Ave
\nSunnyvale, CA 94085\n\n© 2026 Cerebras. \nAll rights reserved."},{"ref":"P16","kind":"page","title":"Nyse Executive Vice Chairman Betty Liu In Conversation With Andrew Feldman Ceo At Cerebras","date":"2026-06-27T16:01:06.850386+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/nyse-executive-vice-chairman-betty-liu-in-conversation-with-andrew-feldman-ceo-at-cerebras","signal_url":null,"signal_json_url":null,"text":"NYSE Executive Vice Chairman, Betty Liu, in conversation with Andrew Feldman, CEO at Cerebras - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nOct 01 2020\nNYSE Executive Vice Chairman, Betty Liu, in conversation with Andrew Feldman, CEO at Cerebras - Cerebras\nRebecca Lewington \n\nFeaturing interviews with NYSE Executive Vice Chairman Betty Liu and business leaders from the comfort of their homes to educate the world on how leaders are pushing their business forward through this global pandemic.\n\nhttps://www.facebook.com/103155091022/posts/10158506533766023/ \n\nhttps://www.linkedin.com/feed/update/urn:li:activity:6717432222690893825/ \n\nhttps://twitter.com/NYSE/status/1311667189972430848 \n\nAbout Cerebras Systems\n\nCerebras Systems is a team of pioneering computer architects, computer scientists, deep learning researchers, and engineers of all types. We have come together to build a new class of computer to accelerate artificial intelligence work by three orders of magnitude beyond the current state of the art. The CS-2 is the fastest AI computer in existence. It contains a collection of industry firsts, including the Cerebras Wafer Scale Engine (WSE-2). The WSE-2 is the largest chip ever built. It contains 2.6 trillion transistors and covers more than 46,225 square millimeters of silicon. The largest graphics processor on the market has 54 billion transistors and covers 815 square millimeters. In artificial intelligence work, large chips process information more quickly producing answers in less time. As a result, neural networks that in the past took months to train, can now train in minutes on the Cerebras CS-2 powered by the WSE-2.\n\nFollow\n\nGet Updates\nNewsletter Signup \n\nCompany\nAbout Us \nCareers \nContact Us \nInvestor Relations \nWebsite Terms of Use \nPrivacy Policy \nCookie Policy \nOther Terms & Policies \nService Status \nTrust Center \n\nNews\nNewsroom \nIn the News \nPress kit \n\nInsights\nCustomer Spotlight \nBlog \nPublications \nWhitepapers \n\nPerformance comparisons are based on third-party benchmarking or internal testing. Observed inference speed improvements versus GPU-based systems may vary depending on workload, configuration, da"},{"ref":"P17","kind":"page","title":"The Complete Guide To Scale Out On Cerebras Wafer Scale Clusters","date":"2026-06-27T16:01:06.307495+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/the-complete-guide-to-scale-out-on-cerebras-wafer-scale-clusters","signal_url":null,"signal_json_url":null,"text":"The Complete Guide to Scale-Out on Cerebras Wafer-Scale Clusters - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nSep 14 2022\nThe Complete Guide to Scale-Out on Cerebras Wafer-Scale Clusters - Cerebras\nDarious Lam \n\nAbstract\nTraining large ML models at scale is difficult because of the complexities involved in 3D parallelism techniques. Moreover, standard scaling methods suffer from bottlenecks at various points in the system, and therefore are unable to reach linear scaling. In this article, I’ll show how Cerebras has solved these challenges through “appliance mode”, enabling push-button linear scaling on Cerebras Wafer-Scale Clusters of up to 192 CS-2 systems.\nPicture this: you’re sitting at your desk, it’s the Friday before a long weekend, and you need to kick off a training run for a GPT-style large NLP model. It’s a multi-billion parameter model, and you’ve just started working on getting it to train on a NVIDIA cluster. The command used to run training is arcane, and your model code is filled with details about chunk sizes and GPU mappings for pipeline parallel. You pray that there are no bugs. The hours march on.\nMeanwhile, your colleague, who used a Cerebras Wafer-Scale Cluster , has long gone home.\nGetting large language models to train at scale is no easy feat on GPUs.\nOften, for experimentation, models are written to train on a single server multi-GPU setup. Once it’s time to scale out, in order to speed up training, multiple servers are involved, and things get tricky. Inter-process communications libraries have to be considered. We have to use batch-scheduling systems. And, because model sizes don’t fit on a single device, and even individual layers don’t fit on a single device, we have to resort to complex training strategies like pipelined or tensor model parallelism (Figure 1). For a deeper dive on parallelism strategies, I highly recommend this post on our developer blog by my colleagues Natalia Vassilieva.\nMany papers have been written about efforts to try to make distributed cluster training faster, such as this one by Narayanan   et al which describes their efforts to train Megatron-LM across more than 3,00"},{"ref":"P18","kind":"page","title":"Meet Jais The Worlds Most Advanced Arabic Large Language Model Open Sourced By G42s Inception","date":"2026-06-27T16:01:06.269248+00:00","date_source":null,"source_url":"https://www.cerebras.ai/press-release/meet-jais-the-worlds-most-advanced-arabic-large-language-model-open-sourced-by-g42s-inception","signal_url":null,"signal_json_url":null,"text":"Meet “Jais”, The World’s Most Advanced Arabic Large Language Model Open Sourced by G42’s Inception - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nAug 29 2023\nMeet “Jais”, The World’s Most Advanced Arabic Large Language Model Open Sourced by G42’s Inception - Cerebras\nUdai Mody \n\nAbu Dhabi, [August 30, 2023] — Inception, the pioneering G42 company dedicated to pushing the boundaries of AI, announced the open-source release of Jais, the world’s highest quality Arabic Large Language Model. Jais is a 13-billion parameter model trained on a newly developed 395-billion-token Arabic and English dataset.\n\nWith a name inspired by UAE’s highest peak, Jais will bring the advantages of generative AI across the Arabic-speaking world. The model is the result of a collaboration between Inception, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) — the world’s first graduate research university dedicated to AI — and Cerebras Systems. It was trained on Condor Galaxy , the recently announced multi-exaFLOP AI supercomputer built by G42 and Cerebras.\n\nJais’ release marks a significant milestone in the realm of AI for the Arabic world. It is a model homegrown in the UAE’s capital, Abu Dhabi, offering more than 400 million Arabic speakers the opportunity to harness the potential of generative AI. It will facilitate and expedite innovation, highlighting Abu Dhabi’s leading position as a hub for AI, innovation, culture preservation, and international collaboration.\n\nBy open-sourcing Jais, Inception aims to engage the scientific, academic, and developer communities to accelerate the growth of a vibrant Arabic language AI ecosystem. This can serve as a model for other languages currently underrepresented in mainstream AI.\n\n“We believe that innovation thrives when we collaborate,” says Andrew Jackson, CEO of Inception. “With this release, we are setting a new standard for AI advancement in the Middle East and ensuring that the Arabic language, with its depth and heritage, finds its voice within the AI landscape. Jais is a testament to our commitment to excellence and our dedication to democratizing AI and promoting innovation.”\n\nJais ou"},{"ref":"P19","kind":"page","title":"Dhiraj Mallick Coo","date":"2026-06-27T16:01:06.216879+00:00","date_source":null,"source_url":"https://www.cerebras.ai/press-release/dhiraj-mallick-coo","signal_url":null,"signal_json_url":null,"text":"Cerebras Systems Promotes Dhiraj Mallick to Chief Operating Officer - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nSep 12 2023\nCerebras Systems Promotes Dhiraj Mallick to Chief Operating Officer - Cerebras\nTin Hoang \n\nSunnyvale, CA—September 11, 2023— Cerebras Systems , the pioneer in accelerating generative AI, today announced it has promoted Dhiraj Mallick to serve as the company’s chief operating officer (COO). Mallick joined Cerebras in 2018 and had previously been senior vice president of engineering and operations. In his expanded role as COO, Mallick will contribute heavily to the company strategy, create and drive operational vision, and streamline operations across business functions. He will also be responsible for the company’s operations and supply chain functions, including systems and hardware development, manufacturing and hardware engineering operations, foundry and supplier management, supply planning and logistics.\n\n“Dhiraj has made a significant impact on our business over the past five years,” said Andrew Feldman, CEO and co-founder, Cerebras Systems. “Given all he has accomplished leading our hardware engineering and operations team, coupled with his diverse and impressive career path, Dhiraj is the ideal leader to serve as our COO. I look forward to working closely with him as we continue to push the boundaries of what’s possible in generative AI.”\n\n“I am honored to be named the COO of Cerebras at such an exciting time in the company’s history,” said Mallick. “Given our recent milestones announcing the world’s most powerful AI supercomputers and leading open-source large language models and datasets, Cerebras is cementing its leadership position in generative AI. I look forward to working closely with Andrew and the rest of the leadership team to continue our momentum.”\n\nMallick has more than two decades of executive leadership experience directing large, high-performing engineering teams at Intel and AMD, as well as startups, including SeaMicro and NexGen. Prior to Cerebras, he was chief technology officer and vice president of architecture for Intel’s $20B data center business. Before Intel, he was an"},{"ref":"P20","kind":"page","title":"Llama 405b Inference","date":"2026-06-27T16:01:06.029189+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/llama-405b-inference","signal_url":null,"signal_json_url":null,"text":"Llama 3.1 405B now runs at 969 tokens/s on Cerebras Inference - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nNov 18 2024\nLlama 3.1 405B now runs at 969 tokens/s on Cerebras Inference - Cerebras\nJames Wang \n\nFrontier AI now runs at instant speed. Last week we ran a customer workload on Llama 3.1 405B at 969 tokens/s – a new record for Meta’s frontier model. Llama 3.1 405B on Cerebras is by far the fastest frontier model in the world – 12x faster than GPT-4o and 18x faster than Claude 3.5 Sonnet. In addition, we achieved the highest performance at 128K context length and shortest time-to-first-token latency, as measured by Artificial Analysis.\n\nLlama 3.1 405B on Cerebras Inference highlights:\n969 output tokens per second – 12x faster than best GPU result\n240ms time to first token – a fraction of most APIs\n128K context length support – highest recorded performance\n16-bit weights – full model accuracy\nQ1 general availability at $6/M input tokens and $12/M output tokens\n\nFrontier AI at Instant Speed\nThis year Cerebras pushed Llama 3.1 8B and 70B to over 2,000 tokens/s, but frontier models such as GPT-4o, Claude 3.5 Sonnet, and Llama 3.1 405B have never exceeded 200 tokens/s on any GPU, ASIC, or cloud. As a result, developers have had to choose between second tier models that run fast or frontier models that run slow. Cerebras Inference fixes this. Responding to a customer request to show Llama 3.1 405B on Cerebras Inference at full 128K context, we deployed a instance that broke every record in output speed, long context performance, and time-to-first-token as measured by Artificial Analysis.\n\nCerebras Inference generated 969 output tokens/s when given a 1,000 token prompt. This is the first time a frontier model is running at instant speed, allowing entire pages of text, code, and math to be complete in a flash. This result was 8x faster than SambaNova, 12x faster than the fastest GPU cloud, and 75x faster than AWS.\n\nWhen the input prompt was extended to 100,000 tokens, only six vendors returned a result with Cerebras being the only non-GPU vendor to complete the benchmark. Cerebras achieved 539 tokens/s – 11x faster than Firewo"},{"ref":"P21","kind":"page","title":"Cerebras Announces Six New Ai Datacenters Across North America And Europe To Deliver Industry S","date":"2026-06-27T16:01:05.880413+00:00","date_source":null,"source_url":"https://www.cerebras.ai/press-release/cerebras-announces-six-new-ai-datacenters-across-north-america-and-europe-to-deliver-industry-s","signal_url":null,"signal_json_url":null,"text":"Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nFeb 11 2025\nCerebras Announces Six New AI Datacenters Across North America and Europe to Deliver Industry’s Largest Dedicated AI Inference Cloud\nSneha Khanvilkar \n\nMarch 11, 2025 — Sunnyvale, CA — Cerebras Systems, the pioneer in accelerating generative AI, today announced the launch of six new AI inference datacenters powered by Cerebras Wafer-Scale Engines. These state-of-the-art facilities, equipped with thousands of Cerebras CS-3 systems, is expected to serve over 40 million Llama 70B tokens per second, thus making Cerebras the world’s #1 provider of high-speed inference and the largest domestic high-speed inference cloud.\nThese new datacenters mark a critical milestone in Cerebras’ 2025 AI inference scaling plan, expanding aggregate capacity by 20x in order to serve surging customer demand. The Oklahoma City and Montreal datacenters house AI hardware exclusively owned and operated by Cerebras. The remaining sites are jointly operated with Cerebras strategic partner G42. With 85% of total capacity located in the United States, Cerebras will play a key role in advancing our nation’s AI infrastructure and leadership.\nCerebras AI Inference Data Centers: \nSanta Clara, CA (online)\nStockton, CA (online)\nDallas, TX (online)\nMinneapolis, MN (Q2 2025)\nOklahoma City, OK (Q3 2025)\nMontreal, Canada (Q3 2025)\nMidwest / Eastern US (Q4 2025)\nEurope (Q4 2025)\n\nSince announcing its high-speed inference offering in August 2024, Cerebras has experienced surging demand from the world’s leading AI companies and enterprises. Mistral, France’s leading AI startup, uses Cerebras to power its flagship Le Chat AI assistant. Perplexity, the world’s leading AI search engine, uses Cerebras to provide instant search results. This month, HuggingFace and AlphaSense, the GitHub of AI and the leading market intelligence platform respectively, both announced they are also adopting Cerebras for its lighting fast inference capability.\n“Cerebras is turbocharging the future of U.S. AI leadership with unmatched performance, scale and efficiency – these new global datacenters will serve as the backbone for the next wa"},{"ref":"P22","kind":"page","title":"Introducing Cerebras Inference Ai At Instant Speed","date":"2026-06-27T16:01:05.68709+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/introducing-cerebras-inference-ai-at-instant-speed","signal_url":null,"signal_json_url":null,"text":"Introducing Cerebras Inference: AI at Instant Speed - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nAug 27 2024\nIntroducing Cerebras Inference: AI at Instant Speed - Cerebras\nJames Wang \n\nToday, we are announcing Cerebras inference – the fastest AI inference solution in the world. Cerebras inference delivers 1,800 tokens per second for Llama3.1 8B and 450 tokens per second for Llama3.1 70B, which is 20x faster than NVIDIA GPU-based hyperscale clouds. Cerebras inference is open to developers today via API access with generous rate limits.\nPowered by the third generation Wafer Scale Engine, Cerebras inference runs Llama3.1 20x faster than GPU solutions at 1/3 the power of DGX solutions. At 1,800 tokens/s, Cerebras Inference is 2.4x faster than Groq in Llama3.1-8B. For Llama3.1-70B, Cerebras is the only platform to enable instant responses at a blistering 450 tokens/sec. All this is achieved using native 16-bit weights for the model, ensuring the highest accuracy responses.\n\nWhy GPU inference feels slow\n\nWhy do responses from large language models (LLMs) trickle in one word at a time, reminiscent of loading webpages on dialup internet? The reason lies in the sequential nature of LLMs and the vast amounts of memory and bandwidth they require. In LLMs, each word generated must be processed through the entire model—all its parameters must be moved from memory to computation. Generating one word takes one pass, generating 100 words requires 100 passes – since each word is dependent on the prior word, this process cannot be run in parallel. Thus to generate a 100 words a second requires moving the model 100 times per second – requiring vast amounts of memory bandwidth.\n\nTake the popular Llama3.1-70B model. The model has 70 billion parameters. Each parameter is 16-bit, requiring 2 bytes of storage. The entire model requires 140GB of memory. For the model to output one token, every parameter must be sent from memory to the compute cores to perform the forward pass inference calculation. Since GPUs only have ~200MB of on-chip memory, the model cannot be stored on-chip and must be sent in its entirety to generate every output token.\nGene"},{"ref":"P23","kind":"page","title":"Building An Ai Powered Search Assistant For Zoom Team Chat","date":"2026-06-27T16:01:05.35546+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/building-an-ai-powered-search-assistant-for-zoom-team-chat","signal_url":null,"signal_json_url":null,"text":"Building an AI-Powered Search Assistant for Zoom Team Chat - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nNov 08 2024\nBuilding an AI-Powered Search Assistant for Zoom Team Chat - Cerebras\nOjus Save \n\nImagine a workday where all the answers you need are just a message away. No more switching between apps, no more digging through files and folders, no more endless searches. Just ask, and the information finds you. That’s the future we’re building with our AI-powered chatbot for Zoom Team Chat. By integrating state-of-the-art language models, neural search capabilities, and the rich context of the Zoom platform, we’re creating an assistant that can understand your questions, find the most relevant information, and deliver accurate answers – all within the flow of your Zoom conversations.\nWhat We’re Building\nThis bot does three key things:\nUnderstands questions asked in Team Chat\nDecides whether to search for current information or answer it from its own knowledgebase\nGenerates accurate, contextual responses\nMaintains conversation history for better context\n\nThe Building Blocks \nThe Building Blocks\nZoom Developer Platform : Provides the APIs to interact with Team Chat Bot. This gives us the foundation for our bot’s interface.\nCerebras : The heart of our system, handling AI inference through their various available models.Cerebras’ architecture is crucial here because it: Makes near-instantaneous decisions about when to search\nProcesses search results and generates responses with minimal latency\nMaintains high quality output despite the speed requirements\nHandles responses for better user experience\n\nExa : Powers our real-time search capability, providing neural search with automatic prompt optimization.\nNode.js & Express : For our server implementation and handling HTTP requests.\n\nWhat’s Zoom Team Chat ?\nBefore we dive into the technical stuff, let me quickly explain what Zoom Team Chat is. While most people know Zoom for video meetings, Zoom also has a full-featured messaging platform built right into the Zoom desktop and mobile apps. Think of it as your workspace hub where you can:\nSend messages and files to colleagues\nCreate "},{"ref":"P24","kind":"page","title":"Cerebras Inference 3x Faster","date":"2026-06-27T16:01:05.350549+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/cerebras-inference-3x-faster","signal_url":null,"signal_json_url":null,"text":"Cerebras Inference now 3x faster: Llama3.1-70B breaks 2,100 tokens/s - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nOct 24 2024\nCerebras Inference now 3x faster: Llama3.1-70B breaks 2,100 tokens/s - Cerebras\nJames Wang \n\nToday we’re announcing the biggest update to Cerebras Inference since launch. Cerebras Inference now runs Llama 3.1-70B at an astounding 2,100 tokens per second – a 3x performance boost over the prior release. For context, this performance is:\n16x faster than the fastest GPU solution\n8x faster than GPUs running Llama3.1-3B, a model 23x smaller\nEquivalent to a new GPU generation’s performance upgrade (H100/A100) in a single software release\n\nFast inference is the key to unlocking the next generation of AI apps. From voice, video, to advanced reasoning, fast inference makes it possible to build responsive, intelligent applications that were previously out of reach. From Tavus revolutionizing video generation to GSK accelerating drug discovery workflows, leading companies are already using Cerebras Inference to push the boundaries of what’s possible. Try Cerebras Inference using chat or API at inference.cerebras.ai.\nBenchmarks\nCerebras Inference has been rigorously tested by Artificial Analysis, a third-party benchmarking organization. We reproduce their performance charts below.\n\nIn output speed per user, Cerebras Inference is in a league of its own – 16x faster than the most optimized GPU solution, 68x faster than hyperscale clouds, and 4-8x faster than other AI accelerators.\n\nTime to first token is critical for real time applications. Cerebras is tied second place in first token latency, showing the advantage of a wafer-scale integrated solution vs. complex networked solutions.\n\nTotal response time – measuring a full turn of input and output – is a good proxy for multi-step agentic workflows. Here Cerebras Inference completes a full request in just 0.4 of a second vs. 1.1 to 4.2 seconds on GPU based solutions. For agents, this means getting up to 10x more work done in the same time. For reasoning models, this enables 10x more reasoning steps without increasing response time.\n\nCerebras Inference running Llama"},{"ref":"P25","kind":"page","title":"Chatting Your Way Through 4500 Neurips Papers With Cerebras","date":"2026-06-27T16:01:05.324078+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/chatting-your-way-through-4500-neurips-papers-with-cerebras","signal_url":null,"signal_json_url":null,"text":"Chatting Your Way Through 4500 NeurIPS papers with Cerebras - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nNov 20 2024\nChatting Your Way Through 4500 NeurIPS papers with Cerebras - Cerebras\nIsaac Tai Daniel Kim \n\nWe built a web app to help people find interesting papers at NeurIPS 2024. Use the app to find papers you’re interested in and ask questions about the papers directly with the help of LLMs. This app uses Cerebras Inference and postgres’ full-text search features to help with information retrieval + synthesis and create an instant, chat experience.\nThe Challenge: RAG is bottlenecked by inference\nRetrieval-Augmented Generation (RAG) makes it easy to build applications that help users digest large datasets like the thousands of papers at NeurIPS in a question-and-answer format. However, its effectiveness is often limited by the speed of inference. Generating responses from LLMs in traditional RAG implementations can be slow, particularly when processing large contexts or answering complex queries.\nThis latency directly impacts usability. Slow inference makes real-time exploration impractical, forcing users to wait for results and breaking the flow of discovery. For RAG to work effectively at scale, inference needs to be fast enough to handle large datasets, like the NeurIPS papers, without delay.\nIndexing NeurIPS Papers in a Vector Database\nCreating a fast chat experience for thousands of NeurIPS papers requires pre-indexing all of the files in a vectorDB. We processed thousands of unstructured academic paper PDFs into a structured, searchable vector database hosted on Supabase.\nData Collection\nOur first step was to gather all the papers from NeurIPS 2024. We identified the JSON data structure from the NeurIPS paper directory by inspecting the network requests in our browser’s developer tools.\n\nFrom here, we obtained the complete list of papers, including metadata such as titles, authors, and direct links to the PDFs. Then, we retrieved the actual PDF files using both the NeurIPS directory and arXiv, the primary repository for many of these papers.\nPreprocessing Data\nTo make the papers searchable by an LLM, we needed t"},{"ref":"P26","kind":"page","title":"Supporting Pytorch On The Cerebras Wafer Scale Engine","date":"2026-06-27T16:01:04.754224+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/supporting-pytorch-on-the-cerebras-wafer-scale-engine","signal_url":null,"signal_json_url":null,"text":"Supporting PyTorch on the Cerebras Wafer-Scale Engine - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nApr 13 2022\nSupporting PyTorch on the Cerebras Wafer-Scale Engine - Cerebras\nEmad Barsoum \n\nThe Cerebras Stack | PyTorch Integration | Cerebras Backend | Cerebras Runtime | User Experience | Learn More \nPyTorch has become the leading machine learning (ML) framework because it is easy to use, easy to debug and because it can express a wide range of ideas. It also has a comprehensive and rapidly growing ecosystem.\nIn the 1.2 version of the Cerebras Software Platform (CSoft), we vastly expand our support for PyTorch. With this in mind, I thought it would be interesting to share the Cerebras approach to supporting PyTorch models. For an accelerator to support an ML framework, it must\nAdhere to the core design principles of the framework wherever possible,\nIt must seek to minimize surprises and complexity for the user and\nIt must integrate seamlessly to the framework’s existing ecosystem.\n\nThere are many ways to support PyTorch, each has its pros and cons. In this blog, we will discuss the Cerebras implementation, our future directions and provide a high-level overview of the Cerebras ML Backend.\n\nThe Cerebras Stack\nThe key difference between our CS-2 system and conventional processors is the sheer scale of our solution. At the heart of our system lies the Wafer-Scale Engine (WSE-2), which is the world largest and fastest AI processor. The WSE-2 contains an astonishing 850,000 AI optimized compute cores and more than 40 Gigabytes of high performance on chip memory. The sheer scale of the computational resources on the WSE-2 drove our PyTorch implementation.\n\nPytorch for conventional processors must work around the weaknesses of those processors, including limited on-chip memory and limited memory bandwidth. This problem is exacerbated exponentially when models don’t fit on a single processor. The efficiency of algorithms tends to decrease as they are split, or “sharded” across many chips, because moving data across those chips is much slower than moving data on a single chip. Writing code for massively parallel systems is difficul"},{"ref":"P27","kind":"page","title":"Getting Started With Pytorch Bert Models On The Cerebras Cs 2 System","date":"2026-06-27T16:01:04.708569+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/getting-started-with-pytorch-bert-models-on-the-cerebras-cs-2-system","signal_url":null,"signal_json_url":null,"text":"Getting Started with PyTorch BERT Models - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nApr 14 2022\nGetting Started with PyTorch BERT Models - Cerebras\nRebecca Lewington \n\nAntonio Kim and Ryan Reece, Machine Learning Engineering | April 14, 2022 \nNatural language processing has taken the machine learning community by storm over the past few years. Transformer architectures such as Pytorch BERT and its many variants have gained great notoriety and adoption as over the past few years as a preferred model for sequence data in a variety of domains.\nIn addition, the PyTorch machine learning framework has also gained immense popularity within the machine learning community due to its intuitive nature and ease-of-use.\nNaturally, this means that there is a huge demand for running transformer models such as BERT using PyTorch. We at Cerebras are constantly expanding our support for PyTorch models to provide a simple and easy way to port existing PyTorch models to run at high performance on Cerebras systems with just a few extra lines of code.\nFor a more detailed look at how the Cerebras Software Platform (CSoft) works, please see our Software Documentation .\nIn this blog, we cover the practical steps to get a PyTorch transformer model like BERT running on the CS-2. For details on how we support PyTorch framework on our architecture, please read the blog “ Supporting PyTorch on the Wafer-Scale Engine .”\nRunning PyTorch on a Cerebras System\nRunning any PyTorch model on a Cerebras system is straightforward. Many convenient wrappers are exposed in our API to adapt existing PyTorch training scripts and run models on a Cerebras system.\nTo start, import the PyTorch module from the Cerebras python environment where the convenience wrappers are housed.\n\nNext, to establish a connection to the system, we need to call initialize. All that is required to pass into this function is the IP address of the Cerebras system you want to connect to:\n\nOnce the connection to the system is established, we need to prepare the model, dataloader and optimizer to be loaded onto the system. Let’s assume that we have these objects predefined as follows:\n\nThis is "},{"ref":"P28","kind":"page","title":"Training Multi Billion Parameter Models On A Single Cerebras System Is Easy","date":"2026-06-27T16:01:04.636296+00:00","date_source":null,"source_url":"https://www.cerebras.ai/blog/training-multi-billion-parameter-models-on-a-single-cerebras-system-is-easy","signal_url":null,"signal_json_url":null,"text":"Training Multi-Billion-Parameter Models on a Single Cerebras System is Easy - Cerebras \nSkip to main content \n\nCerebras Announces First Quarter 2026 Results >>\n\nJun 22 2022\nTraining Multi-Billion-Parameter Models on a Single Cerebras System is Easy - Cerebras\nNatalia Vassilieva \n\nNatalia Vassilieva, Director of Product, Machine Learning | June 22, 2022 \n\nConfiguring models  | Training GPT with 1.3 billion parameters | The pain and suffering of GPU clusters \nSwitching to 6.7 billion parameters | Benefits of training on Cerebras | Summary \nThe Cerebras Software Platform (CSoft) makes it easy to train large-scale Transformer-style natural language processing (NLP) models on a single Cerebras CS-2 system. How easy? Let me show you with GPT-3 XL 1.3B and GPT-3 6.7B parameter models as examples. The same codebase, the same command to launch a training job, just different model configurations. No need to worry about how to distribute the training across multiple conventional devices, no complicated hybrid 3D parallelism. Switching from 1.3B to 6.7B to 13B to 20B model training just by changing a few parameters in a configuration file.\n\nConfiguring the models\nOur first example is GPT-3 XL model with 1.3 billion parameters, and the second example is GPT-3 model with 6.7 billion parameters. Both use the same standard Python implementation relying on TensorFlow. All the main model parameters are defined in an easy-to-read and modify YAML parameter file, which are passed to the python code to define model configuration. You can visit our reference implementation repository to take a look at our TensorFlow implementation of GPT-J model and examples of model configuration YAML files, and we will make GPT-2 and GPT-3 implementations available shortly too. [i] \nEach YAML configuration file contains all the information that a typical researcher would care about: model depth in terms of number of hidden layers (decoder blocks in case of GPT models), hidden layer sizes, number of attention heads, optimizer settings, checkpoint frequency, etc.\nTraining a 1.3 billion parameter GPT-3 model on the CS-2\nCS-2 is a network-attached accelerator. To launch a training job, you need your cod"},{"ref":"E1","kind":"event","title":"Never Loop Without Verifiers","date":"2026-06-24T22:24:18+00:00","date_source":"sitemap.lastmod","source_url":"https://www.cerebras.ai/blog/never-loop-without-verifiers","signal_url":"https://onlylabs.fyi/signals/c3d86dad-ab8b-4380-a85d-3575e3623c76","signal_json_url":"https://onlylabs.fyi/signals/c3d86dad-ab8b-4380-a85d-3575e3623c76/signal.json","text":"post_published · Never Loop Without Verifiers · signal_desk=talking · occurred_at=2026-06-24T22:24:18+00:00 · url=https://www.cerebras.ai/blog/never-loop-without-verifiers"},{"ref":"E2","kind":"event","title":"Hardware / Low Level Security Engineer","date":"2026-06-23T17:42:54+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7782099003","signal_url":"https://onlylabs.fyi/signals/2f2a7db7-8751-42b2-981f-fd010186954c","signal_json_url":"https://onlylabs.fyi/signals/2f2a7db7-8751-42b2-981f-fd010186954c/signal.json","text":"job_opened · Hardware / Low Level Security Engineer · signal_desk=hiring · occurred_at=2026-06-23T17:42:54+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7782099003 · raw={\"location\":\"Remote, California, United States; Sunnyvale CA or Toronto Canada\",\"ats\":\"greenhouse\"}"},{"ref":"E3","kind":"event","title":"Network Security Engineer","date":"2026-06-23T17:42:04+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7782108003","signal_url":"https://onlylabs.fyi/signals/2927488e-8c59-4637-93e4-aea264537eaa","signal_json_url":"https://onlylabs.fyi/signals/2927488e-8c59-4637-93e4-aea264537eaa/signal.json","text":"job_opened · Network Security Engineer · signal_desk=hiring · occurred_at=2026-06-23T17:42:04+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7782108003 · raw={\"location\":\"Remote, California, United States; Sunnyvale CA or Toronto Canada\",\"ats\":\"greenhouse\"}"},{"ref":"E4","kind":"event","title":"Principal Network Security Architect","date":"2026-06-23T17:41:32+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7782127003","signal_url":"https://onlylabs.fyi/signals/04f70d1e-fe6d-47db-8579-27a0f76f67ca","signal_json_url":"https://onlylabs.fyi/signals/04f70d1e-fe6d-47db-8579-27a0f76f67ca/signal.json","text":"job_opened · Principal Network Security Architect · signal_desk=hiring · occurred_at=2026-06-23T17:41:32+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7782127003 · raw={\"location\":\"Remote, California, United States; Sunnyvale CA or Toronto Canada\",\"ats\":\"greenhouse\"}"},{"ref":"E5","kind":"event","title":"Principal AI Security Engineer","date":"2026-06-23T17:40:58+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7782132003","signal_url":"https://onlylabs.fyi/signals/dc22aab4-9997-4b55-9af8-2fcca8e4d643","signal_json_url":"https://onlylabs.fyi/signals/dc22aab4-9997-4b55-9af8-2fcca8e4d643/signal.json","text":"job_opened · Principal AI Security Engineer · signal_desk=hiring · occurred_at=2026-06-23T17:40:58+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7782132003 · raw={\"location\":\"Remote, California, United States; Sunnyvale CA or Toronto Canada\",\"ats\":\"greenhouse\"}"},{"ref":"E6","kind":"event","title":"Moe Guide Calculator","date":"2026-06-22T16:23:50+00:00","date_source":"sitemap.lastmod","source_url":"https://www.cerebras.ai/blog/moe-guide-calculator","signal_url":"https://onlylabs.fyi/signals/f7461bba-6756-43e2-b2ec-01c0555eae8c","signal_json_url":"https://onlylabs.fyi/signals/f7461bba-6756-43e2-b2ec-01c0555eae8c/signal.json","text":"post_published · Moe Guide Calculator · signal_desk=talking · occurred_at=2026-06-22T16:23:50+00:00 · url=https://www.cerebras.ai/blog/moe-guide-calculator"},{"ref":"E7","kind":"event","title":"Senior Front End Design Engineer (Microarchitecture)","date":"2026-06-20T03:45:09+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7779235003","signal_url":"https://onlylabs.fyi/signals/922659dd-54e1-45a1-992d-c5476dc0ba24","signal_json_url":"https://onlylabs.fyi/signals/922659dd-54e1-45a1-992d-c5476dc0ba24/signal.json","text":"job_opened · Senior Front End Design Engineer (Microarchitecture) · signal_desk=hiring · occurred_at=2026-06-20T03:45:09+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7779235003 · raw={\"location\":\"Bengaluru, Karnataka, India\",\"ats\":\"greenhouse\"}"},{"ref":"E8","kind":"event","title":"Software Engineer, Inference Platform ","date":"2026-06-20T01:28:09+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7779234003","signal_url":"https://onlylabs.fyi/signals/fb5862be-284b-473f-9102-85b3bf032ed0","signal_json_url":"https://onlylabs.fyi/signals/fb5862be-284b-473f-9102-85b3bf032ed0/signal.json","text":"job_opened · Software Engineer, Inference Platform  · signal_desk=hiring · occurred_at=2026-06-20T01:28:09+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7779234003 · raw={\"location\":\"Sunnyvale, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E9","kind":"event","title":"Staff Software Engineer, Inference Platform ","date":"2026-06-19T21:24:23+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7779149003","signal_url":"https://onlylabs.fyi/signals/1a07f531-a933-49a8-a012-2eafb8fa69ec","signal_json_url":"https://onlylabs.fyi/signals/1a07f531-a933-49a8-a012-2eafb8fa69ec/signal.json","text":"job_opened · Staff Software Engineer, Inference Platform  · signal_desk=hiring · occurred_at=2026-06-19T21:24:23+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7779149003 · raw={\"location\":\"Sunnyvale, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E10","kind":"event","title":"Sr. Sourcing Manager – Critical Components","date":"2026-06-19T20:17:02+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7656210003","signal_url":"https://onlylabs.fyi/signals/ee8709bd-d09a-4cbb-8763-9135f998091d","signal_json_url":"https://onlylabs.fyi/signals/ee8709bd-d09a-4cbb-8763-9135f998091d/signal.json","text":"job_opened · Sr. Sourcing Manager – Critical Components · signal_desk=hiring · occurred_at=2026-06-19T20:17:02+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7656210003 · raw={\"location\":\"Sunnyvale, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E11","kind":"event","title":"Ai Inference Cybersecurity","date":"2026-06-18T23:21:42+00:00","date_source":"sitemap.lastmod","source_url":"https://www.cerebras.ai/blog/ai-inference-cybersecurity","signal_url":"https://onlylabs.fyi/signals/3bb5ced8-ddd8-4c62-8a83-c3bf3a425793","signal_json_url":"https://onlylabs.fyi/signals/3bb5ced8-ddd8-4c62-8a83-c3bf3a425793/signal.json","text":"post_published · Ai Inference Cybersecurity · signal_desk=talking · occurred_at=2026-06-18T23:21:42+00:00 · url=https://www.cerebras.ai/blog/ai-inference-cybersecurity"},{"ref":"E12","kind":"event","title":"Gemma 4 On Cerebras The Fastest Inference Is Now Multimodal","date":"2026-06-18T16:27:04+00:00","date_source":"sitemap.lastmod","source_url":"https://www.cerebras.ai/blog/gemma-4-on-cerebras-the-fastest-inference-is-now-multimodal","signal_url":"https://onlylabs.fyi/signals/86748ca7-0ec1-4b78-95b3-9880099a4bc6","signal_json_url":"https://onlylabs.fyi/signals/86748ca7-0ec1-4b78-95b3-9880099a4bc6/signal.json","text":"post_published · Gemma 4 On Cerebras The Fastest Inference Is Now Multimodal · signal_desk=talking · occurred_at=2026-06-18T16:27:04+00:00 · url=https://www.cerebras.ai/blog/gemma-4-on-cerebras-the-fastest-inference-is-now-multimodal"},{"ref":"E13","kind":"event","title":"Which Is Faster Gemini 3 5 Flash Or Kimi K2 6 On Cerebras","date":"2026-06-17T21:56:06+00:00","date_source":"sitemap.lastmod","source_url":"https://www.cerebras.ai/blog/which-is-faster-gemini-3-5-flash-or-kimi-k2-6-on-cerebras","signal_url":"https://onlylabs.fyi/signals/20d50872-f9ec-45e5-bc4b-2f4ea89a6154","signal_json_url":"https://onlylabs.fyi/signals/20d50872-f9ec-45e5-bc4b-2f4ea89a6154/signal.json","text":"post_published · Which Is Faster Gemini 3 5 Flash Or Kimi K2 6 On Cerebras · signal_desk=talking · occurred_at=2026-06-17T21:56:06+00:00 · url=https://www.cerebras.ai/blog/which-is-faster-gemini-3-5-flash-or-kimi-k2-6-on-cerebras"},{"ref":"E14","kind":"event","title":"The Economics Of Ai Reasoning","date":"2026-06-17T21:54:50+00:00","date_source":"sitemap.lastmod","source_url":"https://www.cerebras.ai/blog/the-economics-of-ai-reasoning","signal_url":"https://onlylabs.fyi/signals/2c8add0d-0d8c-47f6-8a32-da813bd138b8","signal_json_url":"https://onlylabs.fyi/signals/2c8add0d-0d8c-47f6-8a32-da813bd138b8/signal.json","text":"post_published · The Economics Of Ai Reasoning · signal_desk=talking · occurred_at=2026-06-17T21:54:50+00:00 · url=https://www.cerebras.ai/blog/the-economics-of-ai-reasoning"},{"ref":"E15","kind":"event","title":"ML Systems Performance Engineer","date":"2026-06-16T06:41:30+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7774479003","signal_url":"https://onlylabs.fyi/signals/d012e0e7-5997-464e-a006-b270a8469122","signal_json_url":"https://onlylabs.fyi/signals/d012e0e7-5997-464e-a006-b270a8469122/signal.json","text":"job_opened · ML Systems Performance Engineer · signal_desk=hiring · occurred_at=2026-06-16T06:41:30+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7774479003 · raw={\"location\":\"Bengaluru, Karnataka, India\",\"ats\":\"greenhouse\"}"},{"ref":"E16","kind":"event","title":"Network Engineer ","date":"2026-06-12T15:26:10+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7762739003","signal_url":"https://onlylabs.fyi/signals/6694a82f-c1b3-4c24-8f15-6e2881bbb4cb","signal_json_url":"https://onlylabs.fyi/signals/6694a82f-c1b3-4c24-8f15-6e2881bbb4cb/signal.json","text":"job_opened · Network Engineer  · signal_desk=hiring · occurred_at=2026-06-12T15:26:10+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7762739003 · raw={\"location\":\"Sunnyvale, CA; Toronto, Ontario, Canada\",\"ats\":\"greenhouse\"}"},{"ref":"E17","kind":"event","title":"Applied Machine Learning Research Scientist","date":"2026-06-10T20:57:13+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7655035003","signal_url":"https://onlylabs.fyi/signals/2fcb0767-c8a3-41d1-8716-30806e2c3875","signal_json_url":"https://onlylabs.fyi/signals/2fcb0767-c8a3-41d1-8716-30806e2c3875/signal.json","text":"job_opened · Applied Machine Learning Research Scientist · signal_desk=hiring · occurred_at=2026-06-10T20:57:13+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7655035003 · raw={\"location\":\"Sunnyvale CA or Toronto Canada\",\"ats\":\"greenhouse\"}"},{"ref":"E18","kind":"event","title":"Physical Design Engineer","date":"2026-06-10T15:46:29+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7764007003","signal_url":"https://onlylabs.fyi/signals/9922fd0f-bda3-4e76-aab0-0af69de5dc30","signal_json_url":"https://onlylabs.fyi/signals/9922fd0f-bda3-4e76-aab0-0af69de5dc30/signal.json","text":"job_opened · Physical Design Engineer · signal_desk=hiring · occurred_at=2026-06-10T15:46:29+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7764007003 · raw={\"location\":\"Sunnyvale, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E19","kind":"event","title":"Product Manager, Strategic Verticals ","date":"2026-06-10T13:55:10+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/6585289003","signal_url":"https://onlylabs.fyi/signals/137b459d-c025-4948-941f-0a2a2d3d8250","signal_json_url":"https://onlylabs.fyi/signals/137b459d-c025-4948-941f-0a2a2d3d8250/signal.json","text":"job_opened · Product Manager, Strategic Verticals  · signal_desk=hiring · occurred_at=2026-06-10T13:55:10+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/6585289003 · raw={\"location\":\"San Francisco, California, United States\",\"ats\":\"greenhouse\"}"},{"ref":"E20","kind":"event","title":"Lead Full Stack Machine Learning Engineer","date":"2026-06-10T07:02:36+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7767933003","signal_url":"https://onlylabs.fyi/signals/e418a3cc-748f-4b77-af56-cb287f138ea0","signal_json_url":"https://onlylabs.fyi/signals/e418a3cc-748f-4b77-af56-cb287f138ea0/signal.json","text":"job_opened · Lead Full Stack Machine Learning Engineer · signal_desk=hiring · occurred_at=2026-06-10T07:02:36+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7767933003 · raw={\"location\":\"Bengaluru, Karnataka, India\",\"ats\":\"greenhouse\"}"},{"ref":"E21","kind":"event","title":"Senior / Staff Technical Program Manager - Datacenter Capacity Delivery (E2E) ","date":"2026-06-09T01:22:29+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7762063003","signal_url":"https://onlylabs.fyi/signals/63fc3796-439b-430c-b5ac-d7b851610e36","signal_json_url":"https://onlylabs.fyi/signals/63fc3796-439b-430c-b5ac-d7b851610e36/signal.json","text":"job_opened · Senior / Staff Technical Program Manager - Datacenter Capacity Delivery (E2E)  · signal_desk=hiring · occurred_at=2026-06-09T01:22:29+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7762063003 · raw={\"location\":\"Europe; Remote, California, United States; Sunnyvale, CA; Toronto, Ontario, Canada\",\"ats\":\"greenhouse\"}"},{"ref":"E22","kind":"event","title":"Senior Front End Design Engineer (Microarchitecture)","date":"2026-06-08T19:12:47+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7763907003","signal_url":"https://onlylabs.fyi/signals/e3e04f01-7cfc-49c4-936d-69578dd8e3fb","signal_json_url":"https://onlylabs.fyi/signals/e3e04f01-7cfc-49c4-936d-69578dd8e3fb/signal.json","text":"job_opened · Senior Front End Design Engineer (Microarchitecture) · signal_desk=hiring · occurred_at=2026-06-08T19:12:47+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7763907003 · raw={\"location\":\"Sunnyvale, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E23","kind":"event","title":"Design Verification Engineer","date":"2026-06-08T19:09:23+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7763894003","signal_url":"https://onlylabs.fyi/signals/5bd8c375-4d24-4bf7-b2fc-d80f93e472d2","signal_json_url":"https://onlylabs.fyi/signals/5bd8c375-4d24-4bf7-b2fc-d80f93e472d2/signal.json","text":"job_opened · Design Verification Engineer · signal_desk=hiring · occurred_at=2026-06-08T19:09:23+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7763894003 · raw={\"location\":\"Bengaluru, Karnataka, India\",\"ats\":\"greenhouse\"}"},{"ref":"E24","kind":"event","title":"Cerebras Kimi K2 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Engineer  · signal_desk=hiring · occurred_at=2026-06-05T19:30:41+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7720309003 · raw={\"location\":\"Sunnyvale, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E26","kind":"event","title":"Generating Beautiful Uis","date":"2026-06-04T21:26:15+00:00","date_source":"sitemap.lastmod","source_url":"https://www.cerebras.ai/blog/generating-beautiful-uis","signal_url":"https://onlylabs.fyi/signals/eaca6109-1a26-4bba-a46b-5cd7596cf805","signal_json_url":"https://onlylabs.fyi/signals/eaca6109-1a26-4bba-a46b-5cd7596cf805/signal.json","text":"post_published · Generating Beautiful Uis · signal_desk=talking · occurred_at=2026-06-04T21:26:15+00:00 · url=https://www.cerebras.ai/blog/generating-beautiful-uis"},{"ref":"E27","kind":"event","title":"3D Physical Design 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Engineer","date":"2026-06-04T18:24:18+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7763846003","signal_url":"https://onlylabs.fyi/signals/bcc464f9-7c76-4e91-b37e-64d2f81b0776","signal_json_url":"https://onlylabs.fyi/signals/bcc464f9-7c76-4e91-b37e-64d2f81b0776/signal.json","text":"job_opened · Design Verification Engineer · signal_desk=hiring · occurred_at=2026-06-04T18:24:18+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7763846003 · raw={\"location\":\"Sunnyvale, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E29","kind":"event","title":"Sr. Staff/Staff Design Verification 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Quality Engineer · signal_desk=hiring · occurred_at=2026-06-04T00:00:52+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7720337003 · raw={\"location\":\"Sunnyvale, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E33","kind":"event","title":"Director / Senior Director, Critical Facility Operations ","date":"2026-06-03T01:46:45+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7762028003","signal_url":"https://onlylabs.fyi/signals/3fe67e58-74ae-438f-a14d-8f938a2911e2","signal_json_url":"https://onlylabs.fyi/signals/3fe67e58-74ae-438f-a14d-8f938a2911e2/signal.json","text":"job_opened · Director / Senior Director, Critical Facility Operations  · signal_desk=hiring · occurred_at=2026-06-03T01:46:45+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7762028003 · raw={\"location\":\"Sunnyvale, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E34","kind":"event","title":" LLM Inference Performance & Evals Engineer","date":"2026-06-03T01:46:11+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/6658665003","signal_url":"https://onlylabs.fyi/signals/f4663a60-53e0-4cb5-81bf-88b61d1465e3","signal_json_url":"https://onlylabs.fyi/signals/f4663a60-53e0-4cb5-81bf-88b61d1465e3/signal.json","text":"job_opened ·  LLM Inference Performance & Evals Engineer · signal_desk=hiring · occurred_at=2026-06-03T01:46:11+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/6658665003 · raw={\"location\":\"Toronto, Ontario, Canada\",\"ats\":\"greenhouse\"}"},{"ref":"E35","kind":"event","title":"Head of 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Advanced Technology: Compiler Engineer · signal_desk=hiring · occurred_at=2026-06-03T01:46:11+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7683606003 · raw={\"location\":\"Sunnyvale, CA; Vancouver, British Columbia, Canada\",\"ats\":\"greenhouse\"}"},{"ref":"E37","kind":"event","title":"Senior Hardware Technical Program Manager","date":"2026-06-03T01:46:11+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7675820003","signal_url":"https://onlylabs.fyi/signals/ee83fc88-cd30-485d-a442-893776967d1a","signal_json_url":"https://onlylabs.fyi/signals/ee83fc88-cd30-485d-a442-893776967d1a/signal.json","text":"job_opened · Senior Hardware Technical Program Manager · signal_desk=hiring · occurred_at=2026-06-03T01:46:11+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7675820003 · raw={\"location\":\"Sunnyvale, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E38","kind":"event","title":"Senior Mechanical Engineer","date":"2026-06-03T01:46:11+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7522557003","signal_url":"https://onlylabs.fyi/signals/3717d32e-d7b9-4f1e-8c5f-532e03893f95","signal_json_url":"https://onlylabs.fyi/signals/3717d32e-d7b9-4f1e-8c5f-532e03893f95/signal.json","text":"job_opened · Senior Mechanical Engineer · signal_desk=hiring · occurred_at=2026-06-03T01:46:11+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7522557003 · raw={\"location\":\"Sunnyvale, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E39","kind":"event","title":"Senior ML Software Engineer - Integration & Quality","date":"2026-06-03T01:46:11+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7620384003","signal_url":"https://onlylabs.fyi/signals/5c9e99f8-bced-49b3-8fb1-9a0bf8664c9b","signal_json_url":"https://onlylabs.fyi/signals/5c9e99f8-bced-49b3-8fb1-9a0bf8664c9b/signal.json","text":"job_opened · Senior ML Software Engineer - Integration & Quality · signal_desk=hiring · occurred_at=2026-06-03T01:46:11+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7620384003 · raw={\"location\":\"Sunnyvale CA or Toronto Canada\",\"ats\":\"greenhouse\"}"},{"ref":"E40","kind":"event","title":"Senior Performance Engineer, 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Staff","date":"2026-06-03T01:46:11+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7728797003","signal_url":"https://onlylabs.fyi/signals/e4fb59c1-f3c8-43c2-91fb-e2f2a74878db","signal_json_url":"https://onlylabs.fyi/signals/e4fb59c1-f3c8-43c2-91fb-e2f2a74878db/signal.json","text":"job_opened · Sr. Technical Staff · signal_desk=hiring · occurred_at=2026-06-03T01:46:11+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7728797003 · raw={\"location\":\"Sunnyvale, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E42","kind":"event","title":"Staff Inference ML Runtime Engineer","date":"2026-06-03T01:46:11+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7523546003","signal_url":"https://onlylabs.fyi/signals/d07c5da6-9709-46d4-85ac-73fdba2d1484","signal_json_url":"https://onlylabs.fyi/signals/d07c5da6-9709-46d4-85ac-73fdba2d1484/signal.json","text":"job_opened · Staff Inference ML Runtime Engineer · signal_desk=hiring · occurred_at=2026-06-03T01:46:11+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7523546003 · raw={\"location\":\"Sunnyvale CA or Toronto Canada\",\"ats\":\"greenhouse\"}"},{"ref":"E43","kind":"event","title":"Staff Kernel Optimzation Engineer ","date":"2026-06-03T01:46:11+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7620254003","signal_url":"https://onlylabs.fyi/signals/46492df8-cc80-4df9-b0c7-1e5bfd0fcb1a","signal_json_url":"https://onlylabs.fyi/signals/46492df8-cc80-4df9-b0c7-1e5bfd0fcb1a/signal.json","text":"job_opened · Staff Kernel Optimzation Engineer  · signal_desk=hiring · occurred_at=2026-06-03T01:46:11+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7620254003 · raw={\"location\":\"Remote, California, United States\",\"ats\":\"greenhouse\"}"},{"ref":"E44","kind":"event","title":"Vice President, Creative & Integrated Marketing ","date":"2026-06-03T01:46:11+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7628240003","signal_url":"https://onlylabs.fyi/signals/1bfbf2c7-c8b8-41d4-8353-93d87b5bcd52","signal_json_url":"https://onlylabs.fyi/signals/1bfbf2c7-c8b8-41d4-8353-93d87b5bcd52/signal.json","text":"job_opened · Vice President, Creative & Integrated Marketing  · signal_desk=hiring · occurred_at=2026-06-03T01:46:11+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7628240003 · raw={\"location\":\"Sunnyvale, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E45","kind":"event","title":"Design Validation Test - Lead/Principal 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Kernel Engineer · signal_desk=hiring · occurred_at=2026-06-03T01:46:11+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7486714003 · raw={\"location\":\"Bengaluru, Karnataka, India\",\"ats\":\"greenhouse\"}"},{"ref":"E52","kind":"event","title":"Manufacturing Linux Network Engineer","date":"2026-06-03T01:46:11+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/cerebrassystems/jobs/7629637003","signal_url":"https://onlylabs.fyi/signals/85d93197-5fc0-4a1b-9b94-efb2b2c8b5be","signal_json_url":"https://onlylabs.fyi/signals/85d93197-5fc0-4a1b-9b94-efb2b2c8b5be/signal.json","text":"job_opened · Manufacturing Linux Network Engineer · signal_desk=hiring · occurred_at=2026-06-03T01:46:11+00:00 · url=https://job-boards.greenhouse.io/cerebrassystems/jobs/7629637003 · raw={\"location\":\"Sunnyvale, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E53","kind":"event","title":"Mechanical 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