Can I Run Qwen3.8 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Written by Jakub Rusinowski · Last updated September 11, 2026
Yes
Yes — Qwen3.8-Flash-Next at Q4_K_M needs about 109.7 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB) (~18.3 GB spare), at ~46.2 tok/s (estimated), with room for about 262,144 tokens of context.
Confidence: high · Recommended quantization: Q4_K_M · Estimated speed: ~46.2 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 |
Qwen3.8 on Framework Desktop (Ryzen AI Max+ 395, 128 GB): memory by quantization
| Quant | Memory needed | Fits 128 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 361 GB | ✗ No | — | — | 360 GB |
| Q8_0 | 192.3 GB | ✗ No | — | — | 191.3 GB |
| Q6_K | 148.6 GB | ✗ No | — | — | 147.6 GB |
| Q5_K_M | 128.6 GB | ✗ No | — | — | 127.6 GB |
| Q4_K_M | 109.7 GB | ✓ Yes | 256K | ~46.2 tok/s | 108.7 GB |
| Q3_K_M | 77.7 GB | ✓ Yes | 256K | ~62.1 tok/s | 76.7 GB |
| Q2_K | 60.2 GB | ✓ Yes | 256K | ~76.7 tok/s | 59.2 GB |
Which Qwen3.8 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Qwen3.8-Max | 1457.5 GB | ✗ Too large | — |
| Qwen3.8-Flash-Next | 109.7 GB | ✓ Fits | ~46.2 tok/s |
| Qwen3.8 27B | 18.3 GB | ✓ Fits | ~10.9 tok/s |
What to watch out for
- 1 larger variant of Qwen3.8 does not fit and would need CPU offload or different hardware.
- 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 Qwen3.8 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Yes — Qwen3.8-Flash-Next at Q4_K_M needs about 109.7 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB) (~18.3 GB spare), at ~46.2 tok/s (estimated), with room for about 262,144 tokens of context.
Which quantization of Qwen3.8 should I use on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Q4_K_M — it needs about 109.7 GB of the 128 GB available, downloads as roughly 108.7 GB, and runs at an estimated 46.2 tokens/sec with up to 256K of context.
What limits Qwen3.8 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)
- Qwen3-Coder on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- SmolLM2 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- SmolLM3 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- StarCoder 2 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Aya 3B (Tiny Aya) on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
Qwen3.8 on GPUs
- Qwen3.8 on NVIDIA GeForce RTX 5090
- Qwen3.8 on NVIDIA GeForce RTX 5080
- Qwen3.8 on NVIDIA GeForce RTX 5070 Ti
- Qwen3.8 on NVIDIA GeForce RTX 5070