Can I Run GPT-OSS on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?

Written by Jakub Rusinowski · Last updated August 15, 2026

Technically yes, but not recommended

It loads, but it is not worth running — GPT-oss 120B at Q6_K fits in Framework Desktop (Ryzen AI Max+ 395, 128 GB)'s 128 GB, yet the memory bandwidth limits it to ~1.9 tok/s (estimated), well below usable interactive speed.

Confidence: high · Recommended quantization: Q6_K · Estimated speed: ~1.9 tok/s

Framework Desktop (Ryzen AI Max+ 395, 128 GB) — what it gives a model

Usable memory for models128 GB
Memory bandwidth256 GB/s
Form factorMini PC
Operating systemWindows or Linux
Memory upgradeableNo — soldered

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

QuantMemory neededFits 128 GB?Max contextEst. speedDownload
F16241.4 GB✗ No240 GB
Q8_0128.9 GB✗ No127.5 GB
Q6_K99.8 GB✓ Yes64K~1.9 tok/s98.4 GB
Q5_K_M86.5 GB✓ Yes64K~2.2 tok/s85.1 GB
Q4_K_M73.9 GB✓ Yes64K~2.6 tok/s72.5 GB
Q3_K_M52.6 GB✓ Yes64K~3.7 tok/s51.2 GB
Q2_K40.9 GB✓ Yes64K~4.8 tok/s39.4 GB

Which GPT-OSS sizes fit

VariantNeeds at Q4_K_MFits?Est. speed
GPT-oss 120B73.9 GB✓ Fits~2.6 tok/s
GPT-OSS 20B13.3 GB✓ Fits~15.1 tok/s

What to watch out for

Framework Desktop 128 GB limitations

Recommended setup

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

How these numbers are calculated

FAQ

Can I run GPT-OSS on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?

It loads, but it is not worth running — GPT-oss 120B at Q6_K fits in Framework Desktop (Ryzen AI Max+ 395, 128 GB)'s 128 GB, yet the memory bandwidth limits it to ~1.9 tok/s (estimated), well below usable interactive speed.

Which quantization of GPT-OSS should I use on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?

Q6_K — it needs about 99.8 GB of the 128 GB available, downloads as roughly 98.4 GB, and runs at an estimated 1.9 tokens/sec with up to 64K of context.

What limits GPT-OSS on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?

Memory bandwidth. The model fits, but at 256 GB/s it can only be read fast enough for roughly 1.9 tokens/sec.

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)

GPT-OSS on GPUs

What This Model Is Good At

Model & Tools

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