Can I Run Devstral on 16 GB system RAM?
作者： Jakub Rusinowski · 最后更新： 2026年9月11日
Yes, but it is tight
Yes, but it is tight — Devstral Small 2505 24B at Q3_K_M needs about 12.4 GB of the 12.8 GB usable on 16 GB system RAM, leaving only ~0.4 GB before the runtime starts swapping. Expect ~6.1 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: medium · Recommended quantization: Q3_K_M · Estimated speed: ~6.1 tok/s
Get a personalized upgrade path →
或在 Vast.ai 比较，低至 $0.35/小时 (typical low · varies)
作为亚马逊联盟成员，我们从符合条件的购买中获得收入。云 GPU 链接为推荐链接——我们可能获得佣金，您无需额外付费。
16 GB system RAM — what it gives a model
| Usable memory for models | 12.8 GB |
| Memory bandwidth | 90 GB/s |
Devstral on 16 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 12.8 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 50.1 GB | ✗ No | — | — | 48 GB |
| Q8_0 | 27.6 GB | ✗ No | — | — | 25.5 GB |
| Q6_K | 21.8 GB | ✗ No | — | — | 19.7 GB |
| Q5_K_M | 19.2 GB | ✗ No | — | — | 17 GB |
| Q4_K_M | 16.6 GB | ✗ No | — | — | 14.5 GB |
| Q3_K_M | 12.4 GB | ✓ Yes | 8K | ~6.1 tok/s | 10.2 GB |
| Q2_K | 10 GB | ✓ Yes | 16K | ~7.7 tok/s | 7.9 GB |
Which Devstral sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Devstral-2 123B | 77.9 GB | ✗ Too large | — |
| Devstral Small 2505 24B | 16.6 GB | ✗ Too large | — |
| Devstral Small 2 24B | 16.6 GB | ✗ Too large | — |
What to watch out for
- Only ~0.4 GB of headroom at Q3_K_M: a longer context or a second application can push this into swapping.
- Q3_K_M is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
- 3 larger variants of Devstral do not fit and would need CPU offload or different hardware.
- These figures assume CPU-only inference. Any discrete GPU, even an 8 GB one, will be several times faster for models that fit in its VRAM.
Recommended setup
llama.cpp (CPU build) or Ollama — both run without a GPU
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 12.8 GB of the 16 GB is treated as usable for model weights (80% — the rest is the OS and running applications).
- DDR5-5600 dual channel at 89.6 GB/s peak. CPU decode is assumed to sustain 35% of that peak, because CPU inference is not purely bandwidth-bound — it also spends real time in compute and thread synchronisation. This figure is an assumption, not a fitted constant: no CPU measurement is in the calibration set.
- CPU-only inference: no GPU is assumed. A GPU of any size will beat these figures substantially.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is computed from this model's published attention configuration.
FAQ
Can I run Devstral on 16 GB system RAM?
Yes, but it is tight — Devstral Small 2505 24B at Q3_K_M needs about 12.4 GB of the 12.8 GB usable on 16 GB system RAM, leaving only ~0.4 GB before the runtime starts swapping. Expect ~6.1 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of Devstral should I use on 16 GB system RAM?
Q3_K_M — it needs about 12.4 GB of the 12.8 GB available, downloads as roughly 10.2 GB, and runs at an estimated 6.1 tokens/sec with up to 8K of context.
What limits Devstral on 16 GB system RAM?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
llama.cpp (CPU build) or Ollama — both run without a GPU
Other RAM Capacities
Other Models on 16 GB system RAM
- EXAONE 3.5 on 16 GB system RAM
- Falcon 3 on 16 GB system RAM
- Gemma 2 Family on 16 GB system RAM
- Gemma 3 on 16 GB system RAM
- Gemma 3n on 16 GB system RAM
Devstral on GPUs
- Devstral on NVIDIA GeForce RTX 5090
- Devstral on NVIDIA GeForce RTX 5080
- Devstral on NVIDIA GeForce RTX 5070 Ti
- Devstral on NVIDIA GeForce RTX 5070