Can I Run Codestral on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Written by Jakub Rusinowski · Last updated May 29, 2024
Yes — comfortably
Yes, comfortably — Codestral 22B at Q8_0 needs about 26.3 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~101.7 GB spare and running at ~7.7 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~7.7 tok/s
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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 |
Codestral on Framework Desktop (Ryzen AI Max+ 395, 128 GB): memory by quantization
| Quant | Memory needed | Fits 128 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 47.1 GB | ✓ Yes | 32K | ~4.2 tok/s | 44.4 GB |
| Q8_0 | 26.3 GB | ✓ Yes | 32K | ~7.7 tok/s | 23.6 GB |
| Q6_K | 20.9 GB | ✓ Yes | 32K | ~9.8 tok/s | 18.2 GB |
| Q5_K_M | 18.4 GB | ✓ Yes | 32K | ~11.2 tok/s | 15.7 GB |
| Q4_K_M | 16.1 GB | ✓ Yes | 32K | ~13 tok/s | 13.4 GB |
| Q3_K_M | 12.1 GB | ✓ Yes | 32K | ~17.7 tok/s | 9.5 GB |
| Q2_K | 10 GB | ✓ Yes | 32K | ~22.1 tok/s | 7.3 GB |
What to watch out for
- 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 computed from this model's published attention configuration.
FAQ
Can I run Codestral on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Yes, comfortably — Codestral 22B at Q8_0 needs about 26.3 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~101.7 GB spare and running at ~7.7 tok/s (estimated), with room for about 32,768 tokens of context.
Which quantization of Codestral should I use on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Q8_0 — it needs about 26.3 GB of the 128 GB available, downloads as roughly 23.6 GB, and runs at an estimated 7.7 tokens/sec with up to 32K of context.
What limits Codestral 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)
- Cogito v1 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Command R Family on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Cosmos 3 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- DeepSeek-OCR on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- DeepSeek R1 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
Codestral on GPUs
- Codestral on NVIDIA GeForce RTX 5090
- Codestral on NVIDIA GeForce RTX 5080
- Codestral on NVIDIA GeForce RTX 5070 Ti
- Codestral on NVIDIA GeForce RTX 5070
What This Model Is Good At
Model & Tools
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