Mistral AI Q8_0 56B Parameters

VRAM Requirements for
Mixtral 8x7B Q8_0

To run Mixtral 8x7B locally at Q8_0 quantization, you need at minimum 51.7 GB of GPU VRAM.

51.7 GB
Required VRAM
47.1 GB
File Size
33K tokens
Context Window
56B
Parameters
Estimated VRAM Required
51.7
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 3.7 GB short
Budget $3,200 – $4,000

2× RTX 4090 (48GB) + aggressive quant

48 GB

Use a lower quantization to fit. Viable for testing at this scale.

Balanced $15,000 – $22,000

NVIDIA A100 80GB PCIe

80 GB HBM2e

Single-card 80GB. Industry-standard for large model inference.

Ultimate $25,000 – $40,000

NVIDIA H100 80GB SXM5

80 GB HBM3

State-of-the-art inference. 3× the bandwidth of A100.

📊 VRAM Calculation Breakdown

Model File Size (Q8_0) 47.1 GB
Context Overhead (32,768 tokens × 56B × 2 ÷ 1M) 3.67 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 51.7 GB

Try a Different Quantization

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

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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 32.1 GB file
36.7 GB
Q8_0 Current
51.7 GB
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Frequently Asked Questions

Can I run Mixtral 8x7B Q8_0 on a consumer GPU?
Running Mixtral 8x7B Q8_0 locally requires 51.7 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 Q8_0 quality good enough for production?
Q8_0 produces near-lossless quality compared to FP16. It's widely used in production deployments where quality is critical and you can afford the extra VRAM.