Can I Run Devstral on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Written by Jakub Rusinowski · Last updated August 15, 2026
Yes
Yes — Devstral-2 123B at Q6_K needs about 104.5 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB) (~23.5 GB spare), at ~5.5 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~5.5 tok/s
Framework Desktop (Ryzen AI Max+ 395, 128 GB) — what it gives a model
| Usable memory for models | 128 GB |
| Memory bandwidth | 256 GB/s |
| Form factor | Mini PC |
| Operating system | Windows or Linux |
| Memory upgradeable | No — soldered |
Devstral on Framework Desktop (Ryzen AI Max+ 395, 128 GB): memory by quantization
| Quant | Memory needed | Fits 128 GB? | Max context | Est. speed | Download |
|---|
| F16 | 249.7 GB | ✗ No | — | — | 246 GB |
| Q8_0 | 134.4 GB | ✗ No | — | — | 130.7 GB |
| Q6_K | 104.5 GB | ✓ Yes | 64K | ~5.5 tok/s | 100.9 GB |
| Q5_K_M | 90.9 GB | ✓ Yes | 64K | ~6.4 tok/s | 87.2 GB |
| Q4_K_M | 77.9 GB | ✓ Yes | 64K | ~7.4 tok/s | 74.3 GB |
| Q3_K_M | 56.1 GB | ✓ Yes | 64K | ~10.1 tok/s | 52.4 GB |
| Q2_K | 44.1 GB | ✓ Yes | 64K | ~12.8 tok/s | 40.4 GB |
Which Devstral sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|
| Devstral-2 123B | 77.9 GB | ✓ Fits | ~7.4 tok/s |
| Devstral Small 24B | 16.6 GB | ✓ Fits | ~12.3 tok/s |
| Devstral-2 22B | 15.7 GB | ✓ Fits | ~13.2 tok/s |
What to watch out for
- 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.
- Memory on this machine is not upgradeable, so the configuration you buy is the ceiling for every model you will ever run on it.
Framework Desktop 128 GB limitations
- Memory is soldered LPDDR5X — unusually for Framework, this is the one component that cannot be upgraded.
- ROCm/Vulkan support for Strix Halo is younger than CUDA; check your runtime supports it before buying.
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.
- 128 GB unified memory at 256 GB/s, shared between CPU and GPU.
- 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 Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Yes — Devstral-2 123B at Q6_K needs about 104.5 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB) (~23.5 GB spare), at ~5.5 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of Devstral should I use on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Q6_K — it needs about 104.5 GB of the 128 GB available, downloads as roughly 100.9 GB, and runs at an estimated 5.5 tokens/sec with up to 64K of context.
What limits Devstral on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
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
Other Models on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
Devstral on GPUs
What This Model Is Good At
Model & Tools
← Can I Run It? | Devstral model page | Check your hardware