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How much VRAM does Mistral 7B need at Q4_K_M? Lightweight local LLM

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.

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.

By TechCompare · Updated

Total VRAM required
9.0 GB
Mistral 7B at Q4_K_M
Weights
3.9 GB
7B params
KV cache
4.3 GB
32K tokens, FP16 KV

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Estimated VRAM required

9.0 GB

7B params at Q4_K_M, 32,768 token context, batch 1, inference.

Weights
3.9 GB
KV cache
4.3 GB
Overhead
0.8 GB

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

RTX 3060
Consumer
12 GB
75% used
RTX 4060 Ti 16GB
Consumer
16 GB
56% used
A100 40GB
Datacenter
40 GB
23% used
Apple M3 Max 64GB
Unified
48 GB
19% used

Just barely too small

RTX 4060
Consumer
8 GB
short by 1.0 GB
Same 7B @ Q4_K_M, 32,768 context scenario, pre-loaded. Change the quant, shrink the context, or try a different preset.
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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

The 9 GB total splits as 3.9 GB of Q4 weights, 4.3 GB of FP16 KV cache at the 32K context, and 0.8 GB of overhead, which fits any 12 GB or 16 GB consumer card with buffer. Drop the context to 8K and the cache falls to about 1.1 GB, bringing the total to roughly 5.5 GB, which fits even a 6 GB or 8 GB card. The honest competitive read in 2026: Llama 3.1 8B and Qwen 2.5 7B generally beat Mistral 7B on benchmarks at the same memory footprint, so Mistral 7B earns its place as a well-supported baseline for fine-tuning experiments and lightweight deployments rather than the top-reasoning pick at this size class.

More Mistral scenarios

DeepSeek V4 Pro 1.6T (MoE) at Q4_K_M
DeepSeek V4 Pro 1.6T at Q4_K_M with the full 1M-token context needs about 1012 GB of VRAM with every expert resident - this is the real shape of the model and the number to plan a deployment against.
View details ➜
Llama 4 Scout (17B/109B) at Q4_K_M
Llama 4 Scout at Q4_K_M with its native 10M context needs about 2231 GB of VRAM with all 109B params resident - that's the number you size hardware against.
View details ➜
gpt-oss 20B (MoE) at Q4_K_M
gpt-oss 20B at Q4_K_M with native 128K context needs about 19.4 GB of VRAM with all experts resident, dropping to roughly 9.3 GB with active-only weight loading.
View details ➜

Related guides

Context window explained
How a model context window caps prompt size.
View guide ➜

Frequently asked questions

Is Mistral 7B still worth running in 2026?
Llama 3.1 8B and Qwen 2.5 7B generally outperform it on benchmarks, but Mistral 7B is well-supported and remains a solid baseline for fine-tuning experiments and lightweight deployments.
What's the smallest GPU that runs Mistral 7B?
A 12 GB or 16 GB GPU handles native 32K context with substantial buffer. For 6 GB or 8 GB cards, drop to 8K context which lowers the KV cache and brings total VRAM down to 5.5 GB.
Is Mistral 7B still worth running in 2026?
For workloads where 7B parameters and Apache 2.0 licensing matter, yes. Mistral 7B runs on a 6 GB GPU at Q4 with 32K context and matches or beats Llama 2 13B on most tasks. It's still the standard small-model baseline. For genuinely strong small-model reasoning, Qwen3 8B-A3B or Qwen 2.5 14B usually win head-to-head in 2026 at similar memory.
When does a 7B-class model make sense over a 14B or 32B?
When the constraint is memory, power, or response latency rather than peak capability. An on-device assistant, a Raspberry Pi-adjacent homelab box, a high-throughput classification pipeline: all cases where 7B's footprint at Q4 on a 6 GB card is the entire point. If quality per task is what matters, the 14-32B band usually wins for the memory it asks.

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