Running Small Language Models (SLMs) on CPUs: A Practical Guide for 2025

Learn why SLMs on CPUs are trending, when to use them, and how to deploy one step-by-step with a real example.

Why Small Language Models on CPUs Are Trending

Large Language Models (LLMs) once required expensive GPUs to run inference. But recent advances have opened the door for cost-efficient CPU deployments, especially for smaller models. Three major shifts made this possible:

1. Smarter Models — SLMs are designed for efficiency and keep improving.
2. CPU-Optimized Runtimes — Frameworks like llama.cpp, GGUF, and Intel optimizations deliver near-GPU efficiency.
3. Quantization — Converting models from 16-bit → 8-bit → 4-bit drastically reduces memory needs and speeds up inference with little accuracy loss.

✅ Sweet spots for CPU deployment:

• 8B parameter models quantized to 4-bit
• 4B parameter models quantized to 8-bit

GGUF & Quantization: Why It Matters

For small language models, GGUF format is a game-changer. Instead of juggling multiple conversion tools, GGUF lets you quantize…