Mistral AI Q5_K_M 56B Parameters

VRAM Requirements for
Mixtral 8x7B Q5_K_M

To run Mixtral 8x7B locally at Q5_K_M quantization, you need at minimum 36.7 GB of GPU VRAM.

36.7 GB
Required VRAM
32.1 GB
File Size
33K tokens
Context Window
56B
Parameters
Estimated VRAM Required
36.7
GB
Prosumer / Workstation
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $1,200 – $1,800

2× RTX 3090 (48GB total)

48 GB

Dual-GPU via tensor parallelism. Best cost per GB at this tier.

Balanced $4,000 – $5,500

NVIDIA A6000 (48GB)

48 GB

Single-card 48GB pro GPU. Clean setup, no multi-GPU overhead.

Ultimate $8,000 – $12,000

NVIDIA A100 40GB SXM

40 GB HBM2e

Data-centre HBM2e bandwidth. Dramatically faster throughput.

📊 VRAM Calculation Breakdown

Model File Size (Q5_K_M) 32.1 GB
Context Overhead (32,768 tokens × 56B × 2 ÷ 1M) 3.67 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 36.7 GB

Try a Different Quantization

Use the interactive calculator to compare Mixtral 8x7B across all available formats.

Open Live Calculator →

Mixtral 8x7B — Other Quantizations

Q3_K_M 19.8 GB file
24.4 GB
Q4_K_M 26.4 GB file
31 GB
Q5_K_M Current
36.7 GB
Q8_0 47.1 GB file
51.7 GB
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Frequently Asked Questions

Can I run Mixtral 8x7B Q5_K_M on a consumer GPU?
Running Mixtral 8x7B Q5_K_M locally requires 36.7 GB VRAM, which exceeds consumer GPUs. You'll need prosumer cards like the NVIDIA A6000 (48GB) or an A100 (80GB).
What happens if I don't have enough VRAM?
If your GPU VRAM is insufficient, llama.cpp and similar tools will offload model layers to system RAM (CPU inference). This is much slower — expect 10-50× the generation latency compared to full GPU inference.
Can I use multiple GPUs to run Mixtral 8x7B?
Yes! Tools like llama.cpp, vLLM, and Ollama support tensor parallelism across multiple GPUs. For example, 2× RTX 3090 (24GB each) gives you 48GB total VRAM, which can run many large models.
Is Q5_K_M quality good enough for production?
Q5_K_M is suitable for specialized use cases. Check community benchmarks for specific quality metrics.