Can I Run Qwen 3 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Written by Jakub Rusinowski · Last updated April 28, 2025
Yes — comfortably
Yes, comfortably — Qwen 3 32B at Q8_0 needs about 37.8 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~90.2 GB spare and running at ~5.3 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~5.3 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 |
Qwen 3 on Framework Desktop (Ryzen AI Max+ 395, 128 GB): memory by quantization
| Quant | Memory needed | Fits 128 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 68.5 GB | ✓ Yes | 64K | ~2.9 tok/s | 65.6 GB |
| Q8_0 | 37.8 GB | ✓ Yes | 64K | ~5.3 tok/s | 34.8 GB |
| Q6_K | 29.8 GB | ✓ Yes | 64K | ~6.8 tok/s | 26.9 GB |
| Q5_K_M | 26.2 GB | ✓ Yes | 64K | ~7.8 tok/s | 23.2 GB |
| Q4_K_M | 22.8 GB | ✓ Yes | 64K | ~9 tok/s | 19.8 GB |
| Q3_K_M | 16.9 GB | ✓ Yes | 64K | ~12.4 tok/s | 14 GB |
| Q2_K | 13.7 GB | ✓ Yes | 64K | ~15.6 tok/s | 10.8 GB |
Which Qwen 3 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Qwen 3 235B-A22B (MoE) | 144.3 GB | ✗ Too large | — |
| Qwen 3 32B | 22.8 GB | ✓ Fits | ~9 tok/s |
| Qwen 3 30B-A3B (MoE) | 20 GB | ✓ Fits | ~67.9 tok/s |
| Qwen 3 14B | 11.1 GB | ✓ Fits | ~19.1 tok/s |
| Qwen 3 8B | 7 GB | ✓ Fits | ~32.1 tok/s |
What to watch out for
- 1 larger variant of Qwen 3 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 Qwen 3 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Yes, comfortably — Qwen 3 32B at Q8_0 needs about 37.8 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~90.2 GB spare and running at ~5.3 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of Qwen 3 should I use on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Q8_0 — it needs about 37.8 GB of the 128 GB available, downloads as roughly 34.8 GB, and runs at an estimated 5.3 tokens/sec with up to 64K of context.
What limits Qwen 3 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)
- Qwen 3.5 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Qwen 3.6 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Qwen 3.7 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Qwen3.8 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Qwen3-Coder on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
Qwen 3 on GPUs
- Qwen 3 on NVIDIA GeForce RTX 5090
- Qwen 3 on NVIDIA GeForce RTX 5080
- Qwen 3 on NVIDIA GeForce RTX 5070 Ti
- Qwen 3 on NVIDIA GeForce RTX 5070