The Case For Small Language Model Inference On Arm Cpus

Arcee AI | The Case for Small Language Model Inference on Arm CPUs

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Blog / The Case for Small Language Model Inference on Arm CPUs

The Case for Small Language Model Inference on Arm CPUs Julien Simon ,

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April 17, 2025

Our Chief Evangelist, Julien Simon, explores the advantages and practical applications of running SLM inference on Arm CPUs.

In the dynamic realm of Artificial Intelligence (AI), Small Language Models (SLMs) are emerging as indispensable tools for organizations. Their unique blend of performance, cost-effectiveness, and resource efficiency is reshaping the AI landscape. As the demand for AI-driven solutions escalates across industries,SLMs present a compelling inference scenario on a variety of hardware, including Arm CPUs. This blog post delves into the advantages and practical applications of running SLM inference on Arm CPUs, underscoring how high-efficiency cloud architectures based on Arm CPUs are set to redefine the reach and cost-effectiveness of AI solutions. From LLMs to SLMs

In the past few years, we have witnessed a significant shift in the AI landscape, marked by the rise of large language models (LLMs) that have showcased impressive capabilities in natural language understanding and generation. However, the sheer size and computational demands of these models often render them impractical for many real-world applications. This is where the more compact and efficient small language models step in, maintaining high levels of accuracy while being more practical. Recent advancements in model architecture and optimization techniques, such as knowledge distillation, have made it possible for Virtuoso-Lite , a 10-billion parameter SLM recently released by Arcee AI , to outperform Nova , a 70-billion parameter model also released by Arcee AI in July 2024 and the best open-source model in its size range at the time. This shift toward smaller models is not just about reducing model size; it's about making best-in-class models accessible across a wide range of environments, from edge devices to cloud servers, without the need for expensive AI accelerators. Privacy, Security, and Compliance

One of the most significant advantages of running SLMs is the enhanced privacy, security, and compliance they offer. Data sovereignty and confidentiality are of the utmost importance in many organizations, especially those in regulated industries such as healthcare, finance, and government. These organizations require complete control over their data and models, often hosting them on-premises or within a private cloud environment to mitigate the risk of data breaches and ensure compliance with data protection regulations. For example, a telecom company could use an SLM to analyze network data locally, eliminating the need to transmit sensitive information to an out-of-country location, thus strengthening its security and sovereignty posture. This would also ensure that the analysis is performed in real-time, enabling faster decision-making and proactive network management. Similarly, an on-site model could be used to analyze video feeds from security cameras, identifying anomalies and potential threats quicker without having to share any data with a remote location. Tailored Models Not all AI applications are created equal, and the one-size-fits-all approach to model deployment is becoming less and less viable. SLMs can be tailored to specific tasks and domains, allowing organizations to optimize their models for particular business use cases and to deliver higher returns on investment. This level of customization is crucial to achieve the best possible performance and efficiency, especially when data is highly domain-specific. For instance, in a smart factory, local servers can run tailored SLMs to monitor and predict the maintenance needs of industrial machinery. This unattended operation allows for proactive maintenance scheduling, reducing downtime and extending the life of equipment. Similarly, a local model could be used to analyze images from quality control cameras, quickly identifying defects in real-time and ensuring that only high-quality products are shipped. Cost-Performance Cost efficiency is a key consideration for any technology deployment, and SLMs offer a compelling value proposition. Regardless of the underlying hardware platform they run on, SLMs require fewer computing and memory resources, allowing organizations to reduce their IT spending without compromising prediction quality. Edge devices, in particular, may experience spiky traffic patterns, with periods of intense activity followed by long stretches of idle time. In such environments, the ability to run both the application and the model on the same hardware without the need for a dedicated accelerator can significantly reduce costs.

{{tips}} SLM Inference on CPU Arm CPUs are everywhere, from smartphones and tablets to servers and cloud instances. This ubiquity means that organizations can leverage existing hardware resources to run SLMs without the need for additional, specialized equipment. The ability to run the model and the application on the same hardware is particularly advantageous in resource-constrained environments, where every watt of power and every byte of memory counts. For example, the Arm Neoverse processors provide a scalable and efficient architecture for data center and edge computing. Arm designed the Neoverse processors to deliver high performance and energy efficiency, making them ideal for running SLMs in enterprise environments. These processors support advanced instruction sets that accelerate common deep learning operations, such as matrix multiplication and dot products, further enhancing performance. To demonstrate the cost-efficiency of Arm CPUs compared to other CPUs, Arcee AI ran inference benchmarks on two comparable Amazon EC2 CPU instances: A c8g.8xlarge instance, powered by an AWS Graviton4 CPU (32 vCPUs), itself based on Arm Neoverse V2, and priced at $1.276 an hour (on-demand price in the us-east-1 region). A c7i.8xlarge instance, powered by an Intel(R) Xeon(R) Platinum 8488C CPU (32 CPUs), priced at $1.428 an hour (on-demand price in the us-east-1 region).

We used our Virtuoso-Lite model (10 billion parameters) and quantized it to 4 bits (Q4_0) with the latest source build of the popular llama.cpp open-source project. With 32 vCPUs and at a batch...