Can I Run Devstral Small 2 on a NVIDIA RTX 5000 Ada?
Runs at full precision (f32). Zero quality loss.
14 quantizations fit your 32.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| f32BEST | 29.0 GB | 30.5 GB | 1.8 GB | +3.0 GB |
| fp16 | 15.0 GB | 16.5 GB | 0.9 GB | +17.0 GB |
| Q8_0 | 8.4 GB | 9.9 GB | 25.1 GB | +23.6 GB |
| Q6_K | 6.8 GB | 8.3 GB | 19.4 GB | +25.2 GB |
| Q5_K_M | 6.0 GB | 7.5 GB | 16.8 GB | +26.0 GB |
| Q5_K_S | 5.8 GB | 7.3 GB | 16.3 GB | +26.2 GB |
| Q4_1 | 5.4 GB | 6.9 GB | 14.9 GB | +26.6 GB |
| Q4_K_M | 5.2 GB | 6.7 GB | 14.3 GB | +26.8 GB |
| Q4_K_S | 5.0 GB | 6.5 GB | 13.6 GB | +27.0 GB |
| Q4_0 | 4.9 GB | 6.4 GB | 13.5 GB | +27.1 GB |
| Q3_K_L | 4.1 GB | 5.6 GB | 12.4 GB | +27.9 GB |
| Q3_K_M | 3.9 GB | 5.4 GB | 11.5 GB | +28.1 GB |
| Q3_K_S | 3.7 GB | 5.2 GB | 10.4 GB | +28.3 GB |
| Q2_K | 3.3 GB | 4.8 GB | 8.9 GB | +28.7 GB |
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Top-ranked open-source models that fit in 32.0GB.
FAQ
Can the NVIDIA RTX 5000 Ada run Devstral Small 2?
Yes. The NVIDIA RTX 5000 Ada's 32.0GB of VRAM is enough to run Devstral Small 2 at f32 quantization (29.0GB required).
What's the best quantization to use?
f32 is the highest-precision quantization that fits in your 32.0GB. It uses about 29.0GB of memory and 30.5GB 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.