Can I Run / Devstral 2 / on NVIDIA RTX 5000 Ada
Can I Run Devstral 2 on a NVIDIA RTX 5000 Ada?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
24.0B
GPU memory
32.0GB
Smallest quant
Q4_K_M
Best fit
Q8_0
4 quantizations fit your 32.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 26.5 GB | 28.0 GB | 25.5 GB | +5.5 GB |
| Q6_K | 20.8 GB | 22.3 GB | 19.8 GB | +11.2 GB |
| Q5_K_M | 18.0 GB | 19.5 GB | 17.0 GB | +14.0 GB |
| Q4_K_M | 15.6 GB | 17.1 GB | 14.6 GB | +16.4 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 RTX 5000 Ada →
Top-ranked open-source models that fit in 32.0GB.
FAQ
Can the NVIDIA RTX 5000 Ada run Devstral 2?
Yes. The NVIDIA RTX 5000 Ada's 32.0GB of VRAM is enough to run Devstral 2 at Q8_0 quantization (26.5GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 32.0GB. It uses about 26.5GB of memory and 28.0GB 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.