Can I Run Mistral Small 3.2 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?

Written by Jakub Rusinowski · Last updated September 6, 2026

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

Mistral Small 3.2 on Framework Desktop (Ryzen AI Max+ 395, 128 GB): memory by quantization

Quant	Memory needed	Fits 128 GB?	Max context	Est. speed	Download
F16	49.6 GB	✓ Yes	128K	~4 tok/s	47.2 GB
Q8_0	27.5 GB	✓ Yes	128K	~7.3 tok/s	25.1 GB
Q6_K	21.8 GB	✓ Yes	128K	~9.3 tok/s	19.4 GB
Q5_K_M	19.2 GB	✓ Yes	128K	~10.7 tok/s	16.7 GB
Q4_K_M	16.7 GB	✓ Yes	128K	~12.4 tok/s	14.2 GB
Q3_K_M	12.5 GB	✓ Yes	128K	~17 tok/s	10.1 GB
Q2_K	10.2 GB	✓ Yes	128K	~21.3 tok/s	7.8 GB

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 Mistral Small 3.2 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?

Yes, comfortably — Mistral Small 3.2 24B at Q8_0 needs about 27.5 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~100.5 GB spare and running at ~7.3 tok/s (estimated), with room for about 131,072 tokens of context.

Which quantization of Mistral Small 3.2 should I use on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?

Q8_0 — it needs about 27.5 GB of the 128 GB available, downloads as roughly 25.1 GB, and runs at an estimated 7.3 tokens/sec with up to 128K of context.

What limits Mistral Small 3.2 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

• Mistral Small 3.2 on Mac mini M4 16 GB
• Mistral Small 3.2 on Mac mini M4 Pro 64 GB
• Mistral Small 3.2 on Beelink SER9 32 GB

Other Models on Framework Desktop (Ryzen AI Max+ 395, 128 GB)

• Mistral Small 4 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
• Nemotron 3 Nano Omni on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
• Nemotron 3 Super on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
• Nemotron 70B on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
• Nemotron Cascade 2 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)

Mistral Small 3.2 on GPUs

• Mistral Small 3.2 on NVIDIA GeForce RTX 5090
• Mistral Small 3.2 on NVIDIA GeForce RTX 5080
• Mistral Small 3.2 on NVIDIA GeForce RTX 5070 Ti
• Mistral Small 3.2 on NVIDIA GeForce RTX 5070

What This Model Is Good At

• Best local LLMs for coding
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Model & Tools

• Mistral Small 3.2 model page
• VRAM calculator
• Check your own hardware

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