How much VRAM does Mistral 7B need at Q4_K_M? Lightweight local LLM
Mistral 7B at Q4_K_M needs about 9.0 GB of VRAM at its native 32K context. The 9 GB footprint fits cleanly on common 12 GB or 16 GB GPUs.
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Estimated VRAM required
9.0 GB
7B params at Q4_K_M, 32,768 token context, batch 1, inference.
Estimate accuracy: Weights within ~2%. KV cache within ~5% for standard GQA models, ~10% for MLA (DeepSeek). Real VRAM may vary with framework (vLLM vs llama.cpp vs Transformers), Flash Attention, and driver overhead.
KV cache exceeds model weights: Consider lowering the context length to save on VRAM. Contexts between 8K and 64K are generally more typical for local setups.
Hardware that fits
Just barely too small
How this is calculated
7B at Q4_K_M is about 3.9 GB of weights, 4.3 GB of KV cache, and 0.8 GB of overhead, totaling 9.0 GB.
Verdict
Mistral 7B Q4_K_M is the canonical 'small but useful' local LLM configuration. It's been overtaken on most benchmarks by Llama 3.1 8B and Qwen 2.5 7B, but it's still a fine baseline and it fits anywhere.
More Mistral scenarios
Frequently asked questions
Is Mistral 7B still worth running in 2026?
What's the smallest GPU that runs Mistral 7B?
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