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Run Mixtral 8x22B on H100 80GB

141B parameters on a 80 GB card. It fits at Q3_K_M — here's the VRAM math at every quantization, the speed to expect, and the quality you trade.

verdict · Mixtral 8x22B on H100 80GB
Fits at Q3_K_M
needs 72.8 GB at Q3_K_M · 76.0 GB usable · ~47 tokens/sec
Q4_K_M won't fit here — run it in the cloud
Smallest GPU that fits at Q4_K_M: H200 141GB
Rent H200 141GB by the hour
VRAM needed and fit for Mixtral 8x22B on H100 80GB by quantization.
quantization vram needed fits 80gb? tokens/sec quality
FP16 (full) 338.4 GB ✗ no — Reference quality — no quantization loss.
Q8_0 179.4 GB ✗ no — Near-lossless; rarely worth the extra space over Q6.
Q6_K 138.7 GB ✗ no — Virtually indistinguishable from full precision.
Q5_K_M 116.7 GB ✗ no — Minor loss; an excellent quality-vs-size balance.
Q4_K_M 94.8 GB ✗ no — Small but measurable loss; the popular default.
Q3_K_Mrecommended 72.8 GB ✓ yes ~47 Noticeable degradation; only when you're tight on VRAM.

Weights = params × bytes/weight, +20% for KV cache & runtime; usable VRAM is 95% of nameplate. Tokens/sec is a bandwidth ceiling (3350 GB/s) — real throughput is lower with long context. Try other combinations →

Running Mixtral 8x22B on a H100 80GB

At Q3_K_M, Mixtral 8x22B's weights take about 73 GB, inside the H100 80GB's 76 GB of usable memory. Noticeable degradation; only when you're tight on VRAM. Generation speed is bound by memory bandwidth — the GPU reads the whole model once per token — so expect on the order of 47 tokens/sec before context overhead. To run it at the popular Q4_K_M default you'd want a H200 141GB or larger — or rent one by the hour rather than buy.

Quantization is the lever

Each step down in precision shrinks the model: FP16 needs about 338 GB, Q4_K_M about 95 GB — a 72% reduction for a small, usually acceptable quality cost. Q5_K_M and Q6_K are near-lossless if you have the headroom; drop to Q3 only when you're genuinely out of VRAM. The quantization guide covers the tradeoffs in detail.

Frequently asked questions

Can a H100 80GB run Mixtral 8x22B?

Yes — Mixtral 8x22B fits on a H100 80GB at Q3_K_M (73 GB of the 76 GB usable), with roughly 47 tokens/sec. Higher-precision quants need more VRAM; the table above shows each option.

How much VRAM does Mixtral 8x22B need?

Mixtral 8x22B is a 141B-parameter model. At FP16 that's about 338 GB; at Q4_K_M (the popular default) about 95 GB, including ~20% for the KV cache and runtime. Quantization is the main lever — see the per-quant table above.

Other combinations

Mixtral 8x22B on other GPUs: RTX A6000, NVIDIA L40S, A100 40GB, A100 80GB, H200 141GB, Apple M3 Max 128GB

Other models on the H100 80GB: Qwen2.5 32B, Command R 35B, Mixtral 8x7B, Llama 3.3 70B, Qwen2.5 72B

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