Can I Run / Devstral 2 / on NVIDIA H200
Can I Run Devstral 2 on a NVIDIA H200?
Yes
Runs at full precision (fp16). Zero quality loss.
Model size
24.0B
GPU memory
141GB
Smallest quant
Q4_K_M
Best fit
fp16
5 quantizations fit your 141GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 49.0 GB | 50.5 GB | 48.0 GB | +92.0 GB |
| Q8_0 | 26.5 GB | 28.0 GB | 25.5 GB | +114.5 GB |
| Q6_K | 20.8 GB | 22.3 GB | 19.8 GB | +120.2 GB |
| Q5_K_M | 18.0 GB | 19.5 GB | 17.0 GB | +123.0 GB |
| Q4_K_M | 15.6 GB | 17.1 GB | 14.6 GB | +125.5 GB |
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Full model details
Devstral 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 2?
Yes. The NVIDIA H200's 141GB of VRAM is enough to run Devstral 2 at fp16 quantization (49.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 141GB. It uses about 49.0GB of memory and 50.5GB 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.