I tested 8 LLM models on Linux without using the GPU (itsfoss.com)

A recent exploration into running large language models (LLMs) on CPU-only setups challenged the long-held belief that a decent GPU is necessary for local inference. With advancements in model formats like GGUF and aggressive quantization techniques (such as 4-bit variants), users can now effectively utilize older hardware—like laptops and Raspberry Pis—for LLM deployment. Runtimes such as Llama.cpp have optimized performance on CPUs, allowing models to operate without severe slowdowns. The experiment revealed that while many models can run on a CPU, usability ultimately hinges on throughput, measured in tokens per second—ideal performance begins around 15-30 tokens/sec for a responsive experience. The findings highlight a variety of LLMs that prove practical on lower-end machines. Models between 1B-2B parameters generally strike a solid balance between speed and quality, with Q4_K_M quantization being particularly effective. For instance, TinyLlama 1.1B operates at approximately 25-28 tokens/sec, making it suitable for basic tasks, while larger models like Gemma 4 E2B and OpenHermes 7B offer improved output quality at slower rates of 9.9 and 4.1 tokens/sec, respectively. This research opens the door for broader accessibility in AI development, demonstrating that localized model use is feasible even with limited resources, greatly benefiting those without high-performing hardware.