Can I Run Devstral on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Written by Jakub Rusinowski · Last updated September 11, 2026
Yes, but it is tight
Yes, but it is tight — Devstral 2 22B at Q4_K_M needs about 15.7 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving only ~0.3 GB before the runtime starts swapping. Expect ~28.7 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: low · Recommended quantization: Q4_K_M · Estimated speed: ~28.7 tok/s
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RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 16 GB |
| Memory bandwidth | 576 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Devstral on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM): memory by quantization
| Quant | Memory needed | Fits 16 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 46.4 GB | ✗ No | — | — | 44 GB |
| Q8_0 | 25.8 GB | ✗ No | — | — | 23.4 GB |
| Q6_K | 20.5 GB | ✗ No | — | — | 18 GB |
| Q5_K_M | 18 GB | ✗ No | — | — | 15.6 GB |
| Q4_K_M | 15.7 GB | ✓ Yes | 8K | ~28.7 tok/s | 13.3 GB |
| Q3_K_M | 11.8 GB | ✓ Yes | 16K | ~38.7 tok/s | 9.4 GB |
| Q2_K | 9.6 GB | ✓ Yes | 32K | ~48 tok/s | 7.2 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 | — |
| Devstral 2 22B | 15.7 GB | ✓ Fits | ~28.7 tok/s |
What to watch out for
- Only ~0.3 GB of headroom at Q4_K_M: a longer context or a second application can push this into swapping.
- 3 larger variants of Devstral do not fit and would need CPU offload or different hardware.
- This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
RTX 4090 laptop limitations
- A mobile RTX 4090 carries 16 GB, not the desktop card's 24 GB, and is closer to a desktop 4080 in throughput — which is why it is modelled against that chip here.
- Sustained throughput depends on the chassis power limit; thin laptops throttle well below the quoted figures.
Recommended setup
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 16 GB of VRAM on the NVIDIA GeForce RTX 4090 Laptop GPU at 576 GB/s.
- 32 GB of system RAM available for CPU offload when a model exceeds VRAM.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run Devstral on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Yes, but it is tight — Devstral 2 22B at Q4_K_M needs about 15.7 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving only ~0.3 GB before the runtime starts swapping. Expect ~28.7 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of Devstral should I use on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Q4_K_M — it needs about 15.7 GB of the 16 GB available, downloads as roughly 13.3 GB, and runs at an estimated 28.7 tokens/sec with up to 8K of context.
What limits Devstral on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
- Devstral on MacBook Pro M4 Max 128 GB
- Devstral on MacBook Pro M4 Max 48 GB
- Devstral on MacBook Pro M4 Pro 24 GB
- Devstral on MacBook Air M4 16 GB
Other Models on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- EXAONE 3.5 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- Falcon 3 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- Gemma 2 Family on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- Gemma 3 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- Gemma 3n on RTX 4090 Laptop (16 GB VRAM, 32 GB 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