Can I Run Llama 4.5 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Written by Jakub Rusinowski · Last updated June 26, 2026
Yes, but it is tight
Yes, but it is tight — Llama 4.5 Scout at Q8_0 needs about 119.4 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving only ~8.6 GB before the runtime starts swapping. Expect ~9.7 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: low · Recommended quantization: Q8_0 · Estimated speed: ~9.7 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 |
Llama 4.5 on Framework Desktop (Ryzen AI Max+ 395, 128 GB): memory by quantization
| Quant | Memory needed | Fits 128 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 221.6 GB | ✗ No | — | — | 218 GB |
| Q8_0 | 119.4 GB | ✓ Yes | 32K | ~9.7 tok/s | 115.8 GB |
| Q6_K | 92.9 GB | ✓ Yes | 64K | ~12.2 tok/s | 89.4 GB |
| Q5_K_M | 80.8 GB | ✓ Yes | 128K | ~13.9 tok/s | 77.3 GB |
| Q4_K_M | 69.4 GB | ✓ Yes | 128K | ~15.9 tok/s | 65.8 GB |
| Q3_K_M | 50 GB | ✓ Yes | 128K | ~21.2 tok/s | 46.5 GB |
| Q2_K | 39.4 GB | ✓ Yes | 256K | ~26 tok/s | 35.8 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 Llama 4.5 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Yes, but it is tight — Llama 4.5 Scout at Q8_0 needs about 119.4 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving only ~8.6 GB before the runtime starts swapping. Expect ~9.7 tok/s (estimated), with room for about 32,768 tokens of context.
Which quantization of Llama 4.5 should I use on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?
Q8_0 — it needs about 119.4 GB of the 128 GB available, downloads as roughly 115.8 GB, and runs at an estimated 9.7 tokens/sec with up to 32K of context.
What limits Llama 4.5 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 Models on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- Magistral Small on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- MiMo-V2.5 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- MiniCPM-V on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- MiniMax M2.5 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
- MiniMax M2.7 on Framework Desktop (Ryzen AI Max+ 395, 128 GB)
Llama 4.5 on GPUs
- Llama 4.5 on NVIDIA GeForce RTX 5090
- Llama 4.5 on Apple M4 Max
- Llama 4.5 on Apple M4
- Llama 4.5 on Apple M3 Max
What This Model Is Good At
Model & Tools
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