✅Runs wellfp16 · 4K ctx · batch 1
Run Mixtral-8x7B-v0.1 on H200
At fp16 precision, Mixtral-8x7B-v0.1 (47 B MoE parameters) needs roughly 111.2 GB of VRAM. H200 has 141 GB, leaving 29.8 GB of headroom — plenty of room for longer contexts and larger batch sizes. Expected throughput ≈ 132 tokens/sec on a single card.
79%
VRAM
Runs well
Estimated VRAM (fp16)
111.2 GB
of 141 GB on H200· 29.8 GB free
Tok/sec
132
TFTT
923ms
Power
395W
Rent on GPUniq marketplace
H200
Live prices from verified providers · billed hourly, no commitment.
Memory breakdown (fp16)
Shared Weights
14.00 GBExpert Weights
93.40 GBKV Cache
0.50 GBActivations
2.00 GBFramework Overhead
1.27 GBQuantization comparison
Lower precision = less VRAM with a small quality trade-off. Quality order: FP16 > INT8 > INT4.
| Precision | VRAM | Utilisation | Fits on H200? |
|---|---|---|---|
| FP16 (full precision) | 111.2 GB | 79% | ✅Runs well |
| INT8 (8-bit) | 56.3 GB | 40% | ✅Runs easily |
| INT4 / Q4 (4-bit) | 28.9 GB | 21% | ✅Runs easily |
Frequently asked
- Can H200 run Mixtral-8x7B-v0.1?
- Yes. Mixtral-8x7B-v0.1 needs ≈ 111.2 GB VRAM at fp16 and H200 provides 141 GB. Expected throughput is 132 tokens/sec per GPU.
- How much VRAM does Mixtral-8x7B-v0.1 use?
- About 111.2 GB at fp16, 28.9 GB at INT4 (for a 4K context, batch size 1). Longer contexts add to KV cache size; larger batches increase activations.
- What's the fastest way to run Mixtral-8x7B-v0.1 on H200?
- Use a production inference engine like vLLM, SGLang, or TensorRT-LLM with paged attention — they cut KV cache 2–3× vs the naive estimate and batch multiple requests efficiently. For H200, enable Flash Attention 2.
- Where can I rent a H200?
- GPUniq aggregates live H200 offers from verified providers. You can deploy an instance in about a minute and pay hourly with no commitment.
Other GPUs for Mixtral-8x7B-v0.1
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