Can I Run GPT-OSS on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 sierpnia 2026
Yes, but it is tight
Yes, but it is tight — GPT-OSS 120B at Q8_0 needs about 125.2 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving only ~2.8 GB before the runtime starts swapping. Expect ~31.8 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~31.8 tok/s
Get a personalized upgrade path →
Jako partner Amazon zarabiamy na kwalifikujących się zakupach. Linki do chmurowych GPU są linkami polecającymi — możemy otrzymać prowizję bez dodatkowych kosztów dla Ciebie.
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 |
GPT-OSS on Framework Desktop (Ryzen AI Max+ 395, 128 GB): memory by quantization
| Quant | Memory needed | Fits 128 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 234.7 GB | ✗ No | — | — | 233.6 GB |
| Q8_0 | 125.2 GB | ✓ Yes | 64K | ~31.8 tok/s | 124.1 GB |
| Q6_K | 96.9 GB | ✓ Yes | 128K | ~40.1 tok/s | 95.8 GB |
| Q5_K_M | 83.9 GB | ✓ Yes | 128K | ~45.4 tok/s | 82.8 GB |
| Q4_K_M | 71.6 GB | ✓ Yes | 128K | ~52 tok/s | 70.5 GB |
| Q3_K_M | 50.9 GB | ✓ Yes | 128K | ~69 tok/s | 49.8 GB |
| Q2_K | 39.5 GB | ✓ Yes | 128K | ~83.9 tok/s | 38.4 GB |
Which GPT-OSS sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| GPT-OSS 120B | 71.6 GB | ✓ Fits | ~52 tok/s |
| GPT-OSS 20B | 13.6 GB | ✓ Fits | ~70.5 tok/s |
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 GPT-OSS on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Yes, but it is tight — GPT-OSS 120B at Q8_0 needs about 125.2 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving only ~2.8 GB before the runtime starts swapping. Expect ~31.8 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of GPT-OSS should I use on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Q8_0 — it needs about 125.2 GB of the 128 GB available, downloads as roughly 124.1 GB, and runs at an estimated 31.8 tokens/sec with up to 64K of context.
What limits GPT-OSS 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)
- Granite 3.0 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- IBM Granite 4.0 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- IBM Granite 4.1 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- IBM Granite 4.2 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- InternLM 3 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
GPT-OSS on GPUs
- GPT-OSS on NVIDIA GeForce RTX 5080
- GPT-OSS on NVIDIA GeForce RTX 5070 Ti
- GPT-OSS on NVIDIA GeForce RTX 5070
- GPT-OSS on NVIDIA GeForce RTX 5060 Ti 16GB
What This Model Is Good At
- Best local LLMs for general assistant
- Best local LLMs for document analysis
- Best local LLMs for enterprise assistant