Can I Run Bonsai 27B on 256 GB system RAM?

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 204.8 GB usable on 256 GB system RAM, leaving ~198.5 GB spare and running at ~13.9 tok/s (estimated), with room for about 262,144 tokens of context.

Confidence: medium · Recommended quantization: 1-bit (binary, ~1.125 bpw) · Estimated speed: ~13.9 tok/s

See what else this hardware can run →

Kup ten sprzęt Intel Arc B570 10GB — 10 GB VRAM · 150 W board power 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.

256 GB system RAM — what it gives a model

Usable memory for models	204.8 GB
Memory bandwidth	90 GB/s

Bonsai 27B on 256 GB system RAM: memory by quantization

Quant	Memory needed	Fits 204.8 GB?	Max context	Est. speed	Download
1-bit (binary, ~1.125 bpw)	6.3 GB	✓ Yes	256K	~13.9 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Superseded model. Bonsai 27B has been superseded by Ternary Bonsai 2.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	~3.9 tok/s
Ternary Bonsai 27B	18.8 GB	✓ Fits	~3.9 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.
These figures assume CPU-only inference. Any discrete GPU, even an 8 GB one, will be several times faster for models that fit in its VRAM.

Recommended setup

llama.cpp (CPU build) or Ollama — both run without a GPU

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
204.8 GB of the 256 GB is treated as usable for model weights (80% — the rest is the OS and running applications).
DDR5-5600 dual channel at 89.6 GB/s peak. CPU decode is assumed to sustain 35% of that peak, because CPU inference is not purely bandwidth-bound — it also spends real time in compute and thread synchronisation. This figure is an assumption, not a fitted constant: no CPU measurement is in the calibration set.
CPU-only inference: no GPU is assumed. A GPU of any size will beat these figures substantially.
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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