Can I Run Bonsai 27B on RTX 3090 Desktop (24 GB VRAM, 64 GB 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 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~17.7 GB spare and running at ~112.4 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: medium · Recommended quantization: 1-bit (binary, ~1.125 bpw) · Estimated speed: ~112.4 tok/s
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RTX 3090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 24 GB |
| Memory bandwidth | 936 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Bonsai 27B on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM): memory by quantization
| Quant | Memory needed | Fits 24 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| 1-bit (binary, ~1.125 bpw) | 6.3 GB | ✓ Yes | 64K | ~112.4 tok/s | 3.8 GB |
Which Bonsai 27B sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| 1-bit Bonsai 27B | 18.8 GB | ✓ Fits | ~37.4 tok/s |
| Ternary Bonsai 27B | 18.8 GB | ✓ Fits | ~37.4 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.
RTX 3090 desktop limitations
- The cheapest route to 24 GB of VRAM, and the standard used-market recommendation for local LLMs.
- Older architecture: no FP8 acceleration, and higher idle power than a current card.
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.
- 24 GB of VRAM on the NVIDIA GeForce RTX 3090 at 936 GB/s.
- 64 GB of system RAM available for CPU offload when a model exceeds VRAM.
- 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 Bonsai 27B on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Yes, comfortably — 1-bit Bonsai 27B at 1-bit (binary, ~1.125 bpw) needs about 6.3 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~17.7 GB spare and running at ~112.4 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of Bonsai 27B should I use on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
1-bit (binary, ~1.125 bpw) — it needs about 6.3 GB of the 24 GB available, downloads as roughly 3.8 GB, and runs at an estimated 112.4 tokens/sec with up to 64K of context.
What limits Bonsai 27B on RTX 3090 Desktop (24 GB VRAM, 64 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 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Codestral on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Cogito v1 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Command R Family on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Cosmos 3 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- DeepSeek-OCR on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
Bonsai 27B on GPUs
- Bonsai 27B on NVIDIA GeForce RTX 5070
- Bonsai 27B on NVIDIA GeForce RTX 5060 Ti 8GB
- Bonsai 27B on NVIDIA GeForce RTX 5060
- Bonsai 27B on NVIDIA GeForce RTX 4070 Ti
What This Model Is Good At
Model & Tools
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