Can I Run Bonsai 27B on Framework Desktop (Ryzen AI Max+ 395, 128 GB)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 lipca 2026

Yes — comfortably

Yes, comfortably — 1-bit Bonsai 27B at 1-bit (binary, ~1.125 bpw) needs about 6.3 GB of the 128 GB usable on Framework Desktop (Ryzen AI Max+ 395, 128 GB), leaving ~121.7 GB spare and running at ~37.6 tok/s (estimated), with room for about 262,144 tokens of context.

Confidence: medium · Recommended quantization: 1-bit (binary, ~1.125 bpw) · Estimated speed: ~37.6 tok/s

See what else this hardware can run →

Uruchom w chmurze RTX 4090 w RunPod — od $0.34/godz. · stawka sprawdzona 2026-07

lub porównaj w Vast.ai od $0.35/godz. (typical low · varies)

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

Bonsai 27B on Framework Desktop (Ryzen AI Max+ 395, 128 GB): memory by quantization

Quant	Memory needed	Fits 128 GB?	Max context	Est. speed	Download
1-bit (binary, ~1.125 bpw)	6.3 GB	✓ Yes	256K	~37.6 tok/s	3.8 GB

Superseded model. Bonsai 27B has been superseded by Ternary Bonsai 2. This page is kept for reference; the newer family is a better starting point. View Ternary Bonsai 2 27B →

Which Bonsai 27B sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
1-bit Bonsai 27B	18.8 GB	✓ Fits	~10.9 tok/s
Ternary Bonsai 27B	18.8 GB	✓ Fits	~10.9 tok/s

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.

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