Moonshot AI
Kimi K2.6
Frontera
3.0MDescargas1.4KMe gustaApr 2026Publicado256K tokensContextoModified MITLicencia100 ExcepcionalCalidad
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.
Comenzar
— copia y pega para ejecutar en localCopy-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
Modelos relacionados
Opciones de cuantización
Estimaciones de VRAM por nivel de cuantización
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
Compatibilidad de hardware
Estimaciones de encaje en todo el hardware
Computing compatibility...
Desglose de memoria
Reference: RTX 2060 6GB
Weights610.0 GB
KV Cache7.4 GB
Runtime2.4 GB
Headroom0.6 GB
Preguntas frecuentes
FAQ — Kimi K2.6
Ver también