LLMLocal inference
Mistral 7B
Weight estimates and planning VRAM for running Mistral 7B locally at each quantization level. Compare the lowest reference-cost devices that clear the plan.
Q4 plan
5 GB
Q8 plan
10 GB
FP16 plan
17 GB
Context window
32 k tokens
Planning targets add 15% to weight estimates for runtime buffers and a modest KV cache. Long context or a different backend can require more. Official Mistral model card ↗
01 // GPUs that can run Mistral 7B
Compatible hardware by quantization
Sorted by dated reference-cost estimates from Apr 30, 2026. These are not live offers.
Q4Q4_K_M (4-bit)
4GB weights · plan ≥5GBGPUVRAMPriceTier
Q8Q8_0 (8-bit)
8GB weights · plan ≥10GBGPUVRAMPriceTier
FP16FP16 (full precision)
14GB weights · plan ≥17GBGPUVRAMPriceTier
02 // Frequently asked
Mistral 7B GPU questions
How much VRAM does Mistral 7B need?
Mistral 7B uses approximately 4GB for Q4 weights, 8GB at Q8, or 14GB at FP16. GPU Hunter adds 15% planning headroom for runtime buffers and a modest KV cache, producing targets of 5GB, 10GB, and 17GB respectively. Exact memory use varies by backend and context length.
What is the cheapest GPU to run Mistral 7B?
Using GPU Hunter's 5GB Q4 planning target, the lowest reference-cost single device is the GeForce RTX 3060 12GB (12GB VRAM, dated estimate $249).
Can I run Mistral 7B at FP16?
Potentially. Mistral 7B uses about 14GB for FP16 weights and 17GB under GPU Hunter's planning allowance. Confirm the backend and context requirement before purchasing.
What quantization is best for Mistral 7B?
Q4_K_M uses about 4GB for weights and is the most hardware-accessible option. Q8_0 uses about 8GB and trades more memory for fidelity. FP16 uses about 14GB before runtime and context overhead. The right choice depends on the task, backend, and context window.