Is Running Language Models on CPU Really Viable?

Running inference with large language models typically requires GPUs due to their ability to handle parallel operations for compute-intensive tasks, but their high demand and cost have prompted exploration into using CPUs. Companies have developed small language models (SLMs) that can efficiently run on CPUs, providing benefits such as cost reduction, enhanced security, and the ability to run on widely available hardware. This blog explores how Arcee AI's AFM-4.5B model performs on various CPU architectures, including Intel Sapphire Rapids, AWS Graviton4, and Qualcomm Z1E-80-100, by using techniques like quantization to maintain model accuracy while reducing precision. Despite the limited parallelism of CPUs, advancements in hardware acceleration features, such as Vector Neural Network Instructions (VNNI) and Advanced Matrix Extensions (AMX), along with open-source innovations, have made CPU-based inference increasingly feasible for production use. Although CPUs may not yet match GPUs for high-throughput tasks, they offer viable alternatives for deployments where cost, privacy, or edge computing constraints are critical considerations, marking a promising shift towards more flexible AI model deployment strategies.

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