Moonshot AI
Kimi K2.6
最先端
3.0Mダウンロード1.4KいいねApr 2026公開日256K トークンコンテキストModified MITライセンス100 卓越品質
Kimi K2.6 (1000B parameters) requires approximately 620.4 GB of VRAM with Q4_K_M quantization. As a Mixture of Experts model with 32B active parameters, it uses less memory than its total parameter count suggests. For the best balance of quality and speed, we recommend hardware with at least 714 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 Kimi K2.6 on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "moonshotai/Kimi-K2.6" \
--hf-file "Kimi-K2.6-Q4_K_M.gguf" \
-c 4096 -ngl 99Quick specs
Parameters1000B (32B active)
Architecturemoe (MoE)
Context256K tokens
Modalitytext+vision
Min RAM390 GB
Rec. RAM610 GB (Q4_K_M)
LicenseModified MIT
FamilyKimi
✓ Vision✓ Code✓ Chat✓ Reasoning
About this model
- •1T total params with 32B active per token
- •256K context and native multimodality
- •Designed for long-horizon coding and autonomous agent workflows
関連モデル
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 |
|---|---|---|---|---|
Q2_K | 2 | 390.0 GB | Low | — |
Q3_K_S | 3 | 490.0 GB | Low | — |
NVFP4 | 4 | 560.0 GB | Medium | — |
Q4_K_M | 4 | 610.0 GB | Medium | — |
Q5_K_M | 5 | 720.0 GB | High | — |
Q6_K | 6 | 820.0 GB | High | — |
Q8_0 | 8 | 1070.0 GB | Very High | — |
F16 | 16 | 2050.0 GB | Maximum | — |
Quality benchmarks
Kimi K2.6 benchmark scores
Coding
SWE-bench Verified80.2%
HumanEval+—
Aider Polyglot—
LiveCodeBench89.6%
Reasoning
MMLU-Pro—
GPQA Diamond90.5%
MATH-500—
ARC Challenge—
Source: official · 2026-04-14
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
Weights610.0 GB
KV Cache7.4 GB
Runtime2.4 GB
Headroom0.6 GB
よくある質問
FAQ — Kimi K2.6
関連項目