Is Running Language Models On Cpu Really Viable

Arcee AI | Is Running Language Models on CPU Really Viable?

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Blog / Is Running Language Models on CPU Really Viable?

Is Running Language Models on CPU Really Viable? Andrew Walko ,

Julien Simon ,

Colin Kealty ,

•

July 9, 2025

Running language models on CPUs has been discussed for some time, but delivering accurate results with production-level performance remains unproven. So, is using CPUs for language models truly viable in production?

Running inference with large language models involves thousands of matrix multiplications, tensor operations, and other compute- and memory-intensive tasks. The need to perform these operations in parallel has made GPUs the natural choice for hosting models. However, GPUs come with one major drawback: everyone wants them, and they’re in limited supply, which drives up the cost. This fact has inspired companies like Meta, Microsoft, Alibaba, and Arcee AI to release small language models (SLMs), such as Llama, Phi, Qwen, and AFM, which can run efficiently on low VRAM GPUs. However, even these GPUs can have a prohibitive price tag if not managed properly, which begs the question: can we run SLMs without GPUs? In this blog, we’ll cover how small language models can run on CPUs. Using Arcee AI’s foundation model, AFM-4.5B , we’ll run benchmarks on Intel Sapphire Rapids, AWS Graviton4, and Qualcomm Z1E-80-100 processors at various quantizations, answering common questions along the way. Is CPU inference just about cost? Reducing cost isn’t the only advantage of CPU inference. Having the flexibility to run models on widely available hardware means it’s now possible to host models anywhere you’d like, particularly on your private infrastructure. You won’t need to worry about whether a company is storing your prompts, using your data to train their next model, or where it may be sending your confidential data. This makes it easier to meet your security, compliance, and governance requirements. Running language models on CPUs also opens up the possibility of edge and on-device deployment of models, where GPUs are extremely impractical due to technical and economic reasons.  Running models close to the end user also helps improve the user experience by reducing or even eliminating connectivity issues, solving the painful problem of “What happens if I lose my internet connection?” Is CPU inference possible? First, let’s address the elephant in the room. GPUs have thousands of cores and can handle massive amounts of parallel operations. Meanwhile, server CPUs typically have 64 cores and are designed to execute tasks sequentially. So, how can running models on a CPU deliver acceptable performance? There are three primary reasons: Hardware acceleration features and instruction sets in modern CPU architectures Open-source innovation in quantization and model serving projects, such as llama.cpp , vLLM , and Intel OpenVINO Small language models that continue to shrink yet improve!

Hardware acceleration Recent CPU architectures feature specialized instruction sets that accelerate low-precision matrix multiplications and other neural network operations, significantly enhancing inference throughput. For example, Intel Xeon Sapphire Rapids and Granite Rapids processors support hardware acceleration instruction sets, such as AVX-512 , Vector Neural Network Instructions (VNNI), and Advanced Matrix Extensions (AMX). VNNI helps reduce the number of instructions required for common deep learning operations, such as convolutions and dot products, by fusing multiple steps into a single instruction. AMX further accelerates matrix-heavy workloads by introducing dedicated matrix multiplication hardware and larger tile-based registers. Together, these technologies significantly increase computational throughput and efficiency, making CPUs more competitive for AI inference workloads. Likewise, AWS Graviton4 processors, based on the Arm Neoverse V2 architecture, offer significant improvements in performance and efficiency for a wide range of workloads, including deep learning inference. Graviton4 features enhanced vector processing capabilities through the Scalable Vector Extension (SVE). These vector instructions enable more efficient execution of parallel computations, such as matrix multiplications and tensor operations, which are crucial for running language models. With higher memory bandwidth, Graviton4 makes Arm-based CPUs an increasingly viable option for cost-effective AI inference at scale. Open-Source Innovation Model serving libraries, such as llama.cpp , take advantage of modern CPU instruction sets during inference to accelerate key operations. These libraries operate under constrained compute and memory environments—especially when running on CPUs instead of GPUs—so they employ a variety of clever optimization techniques to maximize efficiency. To reduce memory overhead, models are typically memory-mapped , a technique that allows only the necessary parts of the model to be loaded into RAM on demand rather than keeping the entire model in memory. This enables larger models to run on systems with limited RAM. A notable recent innovation is block interleaved weight repacking , which reorganizes model weights to better align with the wider vector registers and fused instructions available in modern CPUs (such as AVX-512, VNNI, and AMX). This approach significantly improves prompt processing speeds by allowing more efficient use of the CPU’s SIMD (single instruction, multiple data) capabilities. On the compute side, model-serving libraries use multi-threading to parallelize tensor operations across multiple CPU cores. By distributing the workload effectively—often by assigning different layers or chunks of computation to separate threads—they’re able to achieve much higher throughput and better latency, making CPU-based inference increasingly practical for real-world applications. Small language models Of course, none of the hardware or library improvements are relevant if there isn’t a small enough model that can fit within the compute and memory constraints of a CPU and is accurate enough to be used by businesses. Fortunately, over the past few years, models have continued to improve at an impressive rate, and smaller models are now approaching the same accuracy and capability levels as larger models. AFM-4.5B is an example of this. It’s important to note...