{"schema_version":"onlylabs.public_signal.v1","title":"Scaleway Writing: Infrastructures for LLMs in the cloud","description":"Scaleway writing signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/1e2197d6-8c53-4ddf-92c2-bbf0a4e15180","json_url":"https://onlylabs.fyi/signals/1e2197d6-8c53-4ddf-92c2-bbf0a4e15180/signal.json","generated_at":"2026-06-07T21:16:33.946005+00:00","org":{"slug":"scaleway","name":"Scaleway","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/scaleway","dossier_json_url":"https://onlylabs.fyi/labs/scaleway/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/1e2197d6-8c53-4ddf-92c2-bbf0a4e15180","signal_json":"https://onlylabs.fyi/signals/1e2197d6-8c53-4ddf-92c2-bbf0a4e15180/signal.json","source":"https://www.scaleway.com/en/blog/infrastructures-for-llms-in-the-cloud/","lab_dossier":"https://onlylabs.fyi/labs/scaleway","lab_dossier_json":"https://onlylabs.fyi/labs/scaleway/dossier.json","analysis":"https://onlylabs.fyi/analysis/scaleway","analysis_json":"https://onlylabs.fyi/analysis/scaleway/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/scaleway/evidence.json","category":"https://onlylabs.fyi/neoclouds","category_json":"https://onlylabs.fyi/neoclouds.json","category_feed":"https://onlylabs.fyi/neoclouds/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neocloud","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json?category=neocloud","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml?category=neocloud","data_business":null},"answer_pack":{"answer":"Scaleway published Infrastructures for LLMs in the cloud. This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: Infrastructures for LLMs in the cloud Build • Fabien da Silva • 21/02/24 • 6 min read Open source makes LLMs (large language models) available to everyone. 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for this category, keep conclusions tied to category-specific strategy, source evidence, and follow-up questions.","required_sources":[{"label":"signal_json","url":"https://onlylabs.fyi/signals/1e2197d6-8c53-4ddf-92c2-bbf0a4e15180/signal.json","required":true},{"label":"source","url":"https://www.scaleway.com/en/blog/infrastructures-for-llms-in-the-cloud/","required":true},{"label":"dossier_json","url":"https://onlylabs.fyi/labs/scaleway/dossier.json","required":true},{"label":"analysis_evidence_json","url":"https://onlylabs.fyi/analysis/scaleway/evidence.json","required":true},{"label":"topic_signals_json","url":"https://onlylabs.fyi/topics/talking/signals.json?category=neocloud","required":false},{"label":"data_radar_json","url":null,"required":false}],"expected_output":["one-paragraph source-grounded interpretation","category-specific implication","confidence and missing evidence","recommended next source to inspect"],"prompt_seed":"Using only the linked onlylabs JSON, captured source context, and cited evidence, analyze Scaleway's writing signal \"Infrastructures for LLMs in the cloud\" for neocloud strategy."},"semantic_triples":[{"subject":"Scaleway","predicate":"published","object":"Infrastructures for LLMs in the cloud","text":"Scaleway published Infrastructures for LLMs in the cloud."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"is classified as","object":"writing signal","text":"Infrastructures for LLMs in the cloud is classified as writing signal."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"belongs to","object":"talking desk","text":"Infrastructures for LLMs in the cloud belongs to talking desk."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"has evidence coverage","object":"1 captured evidence page","text":"Infrastructures for LLMs in the cloud has evidence coverage 1 captured evidence page."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"has captured page count","object":"1","text":"Infrastructures for LLMs in the cloud has captured page count 1."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"has readable page count","object":"1","text":"Infrastructures for LLMs in the cloud has readable page count 1."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"has related signal count","object":"6","text":"Infrastructures for LLMs in the cloud has related signal count 6."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"has analysis playbook objective","object":"Turn public writing and discussion into a readable map of research themes, product framing, policy posture, launch narratives, and market attention.","text":"Infrastructures for LLMs in the cloud has analysis playbook objective Turn public writing and discussion into a readable map of research themes, product framing, policy posture, launch narratives, and market attention.."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"has source host","object":"scaleway.com","text":"Infrastructures for LLMs in the cloud has source host scaleway.com."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"has lab","object":"Scaleway","text":"Infrastructures for LLMs in the cloud has lab Scaleway."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"has signal desk","object":"talking","text":"Infrastructures for LLMs in the cloud has signal desk talking."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"has source host","object":"scaleway.com","text":"Infrastructures for LLMs in the cloud has source host scaleway.com."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"has author","object":"Fabien da Silva","text":"Infrastructures for LLMs in the cloud has author Fabien da Silva."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"has watch term","object":"Model card","text":"Infrastructures for LLMs in the cloud has watch term Model card."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"has watch term","object":"Data pipeline","text":"Infrastructures for LLMs in the cloud has watch term Data pipeline."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"has watch term","object":"Infrastructure","text":"Infrastructures for LLMs in the cloud has watch term Infrastructure."},{"subject":"Infrastructures for LLMs in the cloud","predicate":"has watch term","object":"Safety and alignment","text":"Infrastructures for LLMs in the cloud has watch term Safety and alignment."}]},"intelligence":{"signal_desk":"talking","answer":"Scaleway published Infrastructures for LLMs in the cloud. 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There are plenty of options available, especially for inference. You’ve probably heard of Hugging Face’s inference library , but there’s also OpenLLM , vLLM , and many others. The main challenge, especially if you’re a company like Mistral AI building new LLMs, is that the architecture of your LLM has to be supported by all these solutions. They need to be able to talk to Hugging Face, to NVIDIA, to OpenLLM and so on. The second challenge is the cost, especially that of the infrastructures you’ll need to scale your LLM deployment. For that, you have different solutions: Choosing the right GPUs (your LLM has to fit with them) Choosing the right techniques: Quantization, which involves reducing the number of bytes used by the variables, so you can fit larger models into smaller memory constraints. That’s a give and take between the two, as that can have impacts on the accuracy of your model and its performance results Fine-tuning methods, like parameter-efficient fine-tuning ( PEFT ). With PEFT methods, you can significantly..."},"evidence_pages":[{"url":"https://www.scaleway.com/en/blog/infrastructures-for-llms-in-the-cloud/","final_url":"https://www.scaleway.com/en/blog/infrastructures-for-llms-in-the-cloud/","title":"Infrastructures for LLMs in the cloud","http_status":200,"content_type":"text/html; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-07T21:16:33.946005+00:00","bytes":192931,"raw_path":"b37201f38c705aa86c4ae9f3e1888a04a5fa4625fc43735407e8a52f9a39d3af.html","content_hash":"d87d027d95ccf9033656d82f8c4a168418282d1209c113c934ba99836ff2fe1e","excerpt_chars":1200,"truncated":true,"excerpt":"Infrastructures for LLMs in the cloud Build • Fabien da Silva • 21/02/24 • 6 min read Open source makes LLMs (large language models) available to everyone. There are plenty of options available, especially for inference. You’ve probably heard of Hugging Face’s inference library , but there’s also OpenLLM , vLLM , and many others. The main challenge, especially if you’re a company like Mistral AI building new LLMs, is that the architecture of your LLM has to be supported by all these solutions. They need to be able to talk to Hugging Face, to NVIDIA, to OpenLLM and so on. The second challenge is the cost, especially that of the infrastructures you’ll need to scale your LLM deployment. For that, you have different solutions: Choosing the right GPUs (your LLM has to fit with them) Choosing the right techniques: Quantization, which involves reducing the number of bytes used by the variables, so you can fit larger models into smaller memory constraints. That’s a give and take between the two, as that can have impacts on the accuracy of your model and its performance results Fine-tuning methods, like parameter-efficient fine-tuning ( PEFT ). 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