Can I run Mistral 7B?

Mistral 7B is the model that first proved a small open-weight LLM could feel genuinely useful, and it remains a sensible pick for anyone who wants a lightweight local chat assistant. At a 4-bit quant it lands around 4.4 GB, so it fits with room to spare on an 8 GB GPU, runs easily within unified memory on any Apple Silicon Mac, and even a 2-bit build at roughly 3 GB is on the table if you are squeezing it onto older hardware. Its Apache 2.0 license is about as permissive as it gets: free to use commercially, no provider strings attached.

In daily use it is quick and steady for chat and short summaries. On an RTX 4090 it pushes about 196 tokens per second at 4-bit, and even a modest RTX 3060 12GB holds around 70 tok/s, which is faster than you can read; an M-series Max sits near 80 tok/s. The 32K context window is comfortable but not enormous, and filling it all the way pushes total memory to roughly 8.4 GB, so on a tight 8 GB card you will want to keep working context modest rather than packing the full window.

The honest caveat is age: released in late 2023, it trails newer small models on harder reasoning and instruction-following, and Mistral Nemo 12B generally handles longer, more demanding prompts better if you can spare the memory. For coding specifically, Qwen 2.5 Coder 7B tends to be the stronger choice at a similar size. Where Mistral 7B still wins is simplicity and reach: it is tiny, fully open under Apache 2.0, and has years of tooling behind the plain 'mistral' Ollama tag, making it a dependable, no-surprises baseline.