Can I Run Bonsai 27B on RTX 4090 Desktop (24 GB VRAM, 64 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 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~17.7 GB spare and running at ~118.7 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: medium · Recommended quantization: 1-bit (binary, ~1.125 bpw) · Estimated speed: ~118.7 tok/s
See what else this hardware can run →
或在 Vast.ai 比较，低至 $0.35/小时 (typical low · varies)
作为亚马逊联盟成员，我们从符合条件的购买中获得收入。云 GPU 链接为推荐链接——我们可能获得佣金，您无需额外付费。
RTX 4090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 24 GB |
| Memory bandwidth | 1008 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Bonsai 27B on RTX 4090 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 | ~118.7 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 | ~40.1 tok/s |
| Ternary Bonsai 27B | 18.8 GB | ✓ Fits | ~40.1 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 4090 desktop limitations
- 24 GB is the sweet spot for 27–32B models at Q4; 70B needs offload or a second 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 4090 at 1008 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 4090 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 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~17.7 GB spare and running at ~118.7 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of Bonsai 27B should I use on RTX 4090 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 118.7 tokens/sec with up to 64K of context.
What limits Bonsai 27B on RTX 4090 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 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Codestral on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Cogito v1 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Command R Family on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Cosmos 3 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- DeepSeek-OCR on RTX 4090 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
← Can I Run It? | Bonsai 27B model page | Check your hardware