Prism ML
Ternary Bonsai 27B
最先端
611.7Kダウンロード1.0KいいねJul 2026公開日262K トークンコンテキストApache 2.0ライセンス87 優秀品質
Ternary Bonsai 27B (27B parameters) requires approximately 9.7 GB of VRAM with Q2_0_G128 quantization. For the best balance of quality and speed, we recommend hardware with at least 12 GB of VRAM.
はじめに
— コピー&ペーストでローカル実行Copy-paste commands to run Ternary Bonsai 27B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "prism-ml/Ternary-Bonsai-27B-gguf" \
--hf-file "Ternary-Bonsai-27B-gguf-Q2_0_G128.gguf" \
-c 4096 -ngl 99Quick specs
Parameters27B
Architecturedense
Context262K tokens
Modalitytext+vision
Min RAM7.2 GB
Rec. RAM7.2 GB (Q2_0_G128)
LicenseApache 2.0
FamilyBonsai
✓ Code✓ Chat✓ Reasoning
About this model
- •~7.2 GB deployed footprint for a 27B model — about 9.4x smaller than FP16.
- •Retains ~95% of FP16 quality: 80.49 average across 15 thinking-mode benchmarks.
- •True 1.71 bits/weight end-to-end — embeddings, attention, MLP and LM head, with no high-precision escape hatches.
- •Scores above a conventional IQ2_XXS build (72.73) at under two-thirds of its footprint.
- •~26 tok/s on an Apple M5 Pro laptop; ships a DSpark drafter for a 1.34x CUDA speedup.
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最適なハードウェア
Ternary Bonsai 27Bのおすすめ
このモデルを実行
量子化オプション
量子化レベル別VRAM推定値
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 | — |
ハードウェア互換性
全ハードウェアの適合度推定
Computing compatibility...
メモリ内訳
Reference: RTX 2060 6GB
Weights7.2 GB
KV Cache1.0 GB
Runtime0.9 GB
Headroom0.6 GB
よくある質問
FAQ — Ternary Bonsai 27B
関連項目