Can You Run LLMs Locally Without a GPU? I Tested 8 Models on Linux
Can You Run LLMs Locally Without a GPU? I Tested 8 Models on Linux
https://itsfoss.com/testing-local-llms-without-gpu/
Publish Date: 2026-05-15 03:35:00
Source Domain: itsfoss.com
For the longest time, I assumed running LLMs locally needed a decent GPU. That’s what most guides implied, and honestly, that’s how the ecosystem felt not too long ago. But after digging into recent tools and actually trying things out on CPU-only setups, that assumption doesn’t really hold anymore.
Newer model formats like GGUF and aggressive quantization (think 4-bit variants) have made these models much smaller and lighter. At the same time, runtimes such as Llama.cpp have become efficient enough that CPUs (yes, even older ones) can run them without completely falling apart.
That said, I quickly realized something more important: just because a model runs doesn’t mean it’s usable.
While testing, I found that the real metric that matters isn’t model size or even RAM usage, it’s actually tokens per second. A model providing a response at 3–5 tokens per second technically works, but it feels painfully slow in practice. On the other hand, once you get into the 15–30 tok/s range, things start to feel responsive enough for everyday use.
So instead of just listing models that can run on CPU, I focused on ones that are actually usable on low-end machines. This list is based on my own experimentation.
If you’re working with an older laptop, Raspberry Pi, or basic desktop, this guide would be helpful for running your local AI model successfully and speedily.
What “Runs well on CPU” actually means
CPU performance varies wildly depending on model size and quantization. Formats used by tools like llama.cpp let you run models in reduced precision. Q8 offers better quality but is slower than Q4_K, which is much faster but comes with slightly reduced quality.
I found models ranging from ~40+ tokens/sec for tiny models all the way down to ~4 tokens/sec for larger 4B models. It completely changes how usable a model feels.
I would say, 1B-2B models consistently offer the best balance. They’re small enough to fit comfortably…
