Prism ML
1-bit Bonsai 27B
前沿
2.1M下载量633点赞Jul 2026发布日期262K tokens上下文Apache 2.0许可证82 优秀质量
1-bit Bonsai 27B (27B parameters) requires approximately 6.4 GB of VRAM with Q1_0_G128 quantization. For the best balance of quality and speed, we recommend hardware with at least 8 GB of VRAM.
快速开始
— 复制粘贴即可本地运行Copy-paste commands to run 1-bit Bonsai 27B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "prism-ml/Bonsai-27B-gguf" \
--hf-file "Bonsai-27B-gguf-Q1_0_G128.gguf" \
-c 4096 -ngl 99Quick specs
Parameters27B
Architecturedense
Context262K tokens
Modalitytext+vision
Min RAM3.9 GB
Rec. RAM3.9 GB (Q1_0_G128)
LicenseApache 2.0
FamilyBonsai
✓ Code✓ Chat✓ Reasoning
About this model
- •~3.9 GB deployed footprint for a 27B model — about 14.2x smaller than FP16.
- •Retains ~90% of FP16 quality: 76.11 average across 15 thinking-mode benchmarks.
- •True 1.125 bits/weight end-to-end; the vision tower ships in compact 4-bit HQQ.
- •Keeps thinking, reasoning and agentic behaviour in the sub-4-bit regime where conventional low-bit builds collapse.
- •~44 tok/s on an Apple M5 Pro laptop, via custom 1-bit llama.cpp kernels (CUDA, Metal).
相关模型
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最佳硬件
1-bit Bonsai 27B 的最佳选择
运行此模型
量化选项
各量化级别的 VRAM 估算
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q1_0_G128 | 1.125 | 3.9 GB | Very Low | — |
Q2_0_G128 | 1.71 | 7.2 GB | Low | — |
Q2_K | 2 | 10.5 GB | Low | — |
Q3_K_S | 3 | 13.2 GB | Low | — |
NVFP4 | 4 | 15.1 GB | Medium | — |
Q4_K_M | 4 | 16.5 GB | Medium | — |
Q5_K_M | 5 | 19.4 GB | High | — |
Q6_K | 6 | 22.1 GB | High | — |
Q8_0 | 8 | 28.9 GB | Very High | — |
F16 | 16 | 55.4 GB | Maximum | — |
硬件兼容性
全部硬件的适配估算
Computing compatibility...
内存详细分析
Reference: RTX 2060 6GB
Weights3.9 GB
KV Cache1.0 GB
Runtime0.9 GB
Headroom0.6 GB
常见问题
FAQ — 1-bit Bonsai 27B
另请参阅