Can I Run / Devstral Small 2 / on NVIDIA H200
Can I Run Devstral Small 2 on a NVIDIA H200?
Yes
Runs at full precision (fp16). Zero quality loss.
Model size
7.0B
GPU memory
141GB
Smallest quant
Q4_K_M
Best fit
fp16
5 quantizations fit your 141GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 15.0 GB | 16.5 GB | 14.0 GB | +126.0 GB |
| Q8_0 | 8.4 GB | 9.9 GB | 7.4 GB | +132.6 GB |
| Q6_K | 6.8 GB | 8.3 GB | 5.8 GB | +134.2 GB |
| Q5_K_M | 6.0 GB | 7.5 GB | 5.0 GB | +135.0 GB |
| Q4_K_M | 5.2 GB | 6.7 GB | 4.2 GB | +135.8 GB |
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Full model details
Devstral Small 2 →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA H200 →
Top-ranked open-source models that fit in 141GB.
FAQ
Can the NVIDIA H200 run Devstral Small 2?
Yes. The NVIDIA H200's 141GB of VRAM is enough to run Devstral Small 2 at fp16 quantization (15.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 141GB. It uses about 15.0GB of memory and 16.5GB recommended for comfortable inference.
What if I need more headroom for context length?
KV cache memory grows with context length. The numbers above assume a baseline 2K-4K context. For long-context use (32K+), add another 2-6GB depending on the model architecture.