Can I Run Bonsai 27B on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?

作者: Jakub Rusinowski · 最后更新: 2026年7月15日

Yes — comfortably

Yes, comfortably — 1-bit Bonsai 27B at 1-bit (binary, ~1.125 bpw) needs about 6.3 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving ~9.7 GB spare and running at ~61.9 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: medium · Recommended quantization: 1-bit (binary, ~1.125 bpw) · Estimated speed: ~61.9 tok/s

See what else this hardware can run →

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RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM) — what it gives a model

Usable memory for models16 GB
Memory bandwidth448 GB/s
Form factorDesktop
Operating systemWindows or Linux
Memory upgradeableYes

Bonsai 27B on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM): memory by quantization

QuantMemory neededFits 16 GB?Max contextEst. speedDownload
1-bit (binary, ~1.125 bpw)6.3 GB✓ Yes32K~61.9 tok/s3.8 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Superseded model. Bonsai 27B has been superseded by Ternary Bonsai 2.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 9B →

Which Bonsai 27B sizes fit

VariantNeeds at Q4_K_MFits?Est. speed
1-bit Bonsai 27B18.8 GB✗ Too large—
Ternary Bonsai 27B18.8 GB✗ Too large—

What to watch out for

RTX 5060 Ti 16 GB desktop limitations

Recommended setup

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

How these numbers are calculated

FAQ

Can I run Bonsai 27B on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?

Yes, comfortably — 1-bit Bonsai 27B at 1-bit (binary, ~1.125 bpw) needs about 6.3 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving ~9.7 GB spare and running at ~61.9 tok/s (estimated), with room for about 32,768 tokens of context.

Which quantization of Bonsai 27B should I use on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?

1-bit (binary, ~1.125 bpw) — it needs about 6.3 GB of the 16 GB available, downloads as roughly 3.8 GB, and runs at an estimated 61.9 tokens/sec with up to 32K of context.

What limits Bonsai 27B on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?

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 RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)

Bonsai 27B on GPUs

What This Model Is Good At

Model & Tools

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