✅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 95.3 GB of VRAM. H200 has 141 GB, leaving 45.7 GB of headroom — plenty of room for longer contexts and larger batch sizes. Expected throughput ≈ 162 tokens/sec on a single card.
68%
VRAM
Runs well
Estimated VRAM (fp16)
95.3 GB
of 141 GB on H200· 45.7 GB free
Tok/sec
162
TFTT
175ms
Power
386W
Rent on GPUniq marketplace
H200
Live prices from verified providers · billed hourly, no commitment.
Memory breakdown (fp16)
Quantization 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) | 95.3 GB | 68% | ✅Runs well |
| INT8 (8-bit) | 48.8 GB | 35% | ✅Runs easily |
| INT4 / Q4 (4-bit) | 25.6 GB | 18% | ✅Runs easily |
Frequently asked
- Can H200 run Mixtral-8x7B-v0.1?
- Yes. Mixtral-8x7B-v0.1 needs ≈ 95.3 GB VRAM at fp16 and H200 provides 141 GB. Expected throughput is 162 tokens/sec per GPU.
- How much VRAM does Mixtral-8x7B-v0.1 use?
- About 95.3 GB at fp16, 25.6 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
Try it live
Chat with Mixtral-8x7B-v0.1 — no setup
Send a prompt and see how Mixtral-8x7B-v0.1 responds directly in our chat. No installation, no GPU required to test.
Want to tweak sequence length, batch size, or fine-tuning? Open the full calculator →