Can I Run Mistral Small 4 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?

作者： Jakub Rusinowski · 最后更新： 2026年3月16日

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

Quant	Memory needed	Fits 128 GB?	Max context	Est. speed	Download
F16	241.7 GB	✗ No	—	—	238 GB
Q8_0	130.1 GB	✗ No	—	—	126.4 GB
Q6_K	101.2 GB	✓ Yes	64K	~9.6 tok/s	97.6 GB
Q5_K_M	88 GB	✓ Yes	64K	~11 tok/s	84.3 GB
Q4_K_M	75.5 GB	✓ Yes	128K	~12.7 tok/s	71.8 GB
Q3_K_M	54.4 GB	✓ Yes	128K	~17.1 tok/s	50.7 GB
Q2_K	42.8 GB	✓ Yes	128K	~21.1 tok/s	39.1 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 4 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?

Yes — Mistral Small 4 119B-A6.5B at Q6_K needs about 101.2 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB) (~26.8 GB spare), at ~9.6 tok/s (estimated), with room for about 65,536 tokens of context.

Which quantization of Mistral Small 4 should I use on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?

Q6_K — it needs about 101.2 GB of the 128 GB available, downloads as roughly 97.6 GB, and runs at an estimated 9.6 tokens/sec with up to 64K of context.

What limits Mistral Small 4 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 4 on Mac mini M4 Pro 64 GB

Other Models 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)
• Nex-N2 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)

Mistral Small 4 on GPUs

• Mistral Small 4 on NVIDIA GeForce RTX 5090
• Mistral Small 4 on Apple M4 Max
• Mistral Small 4 on Apple M4
• Mistral Small 4 on Apple M3 Max

What This Model Is Good At

• Best local LLMs for coding
• Best local LLMs for reasoning
• Best local LLMs for agents

Model & Tools

• Mistral Small 4 model page
• VRAM calculator
• Check your own hardware

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