Kimi K2.6 — Hardware Requirements & GPU Compatibility
Vision
Kimi K2.6 is a 1058.6B-parameter open language model from Moonshot AI in the Kimi K2 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 639.04 GB of VRAM — see which GPUs and Macs can run it below.
1.2M downloads
1.5K likesSpecifications
- Publisher
- Moonshot AI
- Family
- Kimi K2
- Parameters
- 1058.6B
- Architecture
- KimiK25ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 163,840
- Release Date
- 2026-04-14
- License
- Other
Get Started
HuggingFace
How Much VRAM Does Kimi K2.6 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 453.8 GB | 908.7 GB | 449.90 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 519.9 GB | 974.9 GB | 516.06 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 639.0 GB | 1093.9 GB | 635.15 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 758.1 GB | 1213.0 GB | 754.24 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 877.2 GB | 1332.1 GB | 873.34 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1062.5 GB | 1517.4 GB | 1058.59 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Kimi K2.6?
Q4_K_M · 639.0 GBKimi K2.6 (Q4_K_M) requires 639.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 831+ GB is recommended. Using the full 262K context window can add up to 454.9 GB, bringing total usage to 1093.9 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Kimi K2.6?
Q4_K_M · 639.0 GB2 devices with unified memory can run Kimi K2.6, including NVIDIA DGX H100.
Decent
— Enough memory, may be tightWhere to Download Kimi K2.6
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Kimi K2.6 need?
Kimi K2.6 requires 639.0 GB of VRAM at Q4_K_M, or 2121.1 GB at BF16. Full 262K context adds up to 454.9 GB (1093.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1058.6B × 4.8 bits ÷ 8 = 635.2 GB
KV Cache + Overhead ≈ 3.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 458.7 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M639.0 GBQ4_K_M + full context1093.9 GB- Can NVIDIA GeForce RTX 5090 run Kimi K2.6?
No — Kimi K2.6 requires at least 295.0 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Kimi K2.6?
For Kimi K2.6, Q4_K_M (639.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (758.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 295.0 GB.
VRAM requirement by quantization
IQ2_XXS295.0 GBQ2_K453.8 GBQ3_K_L546.4 GBQ4_K_M ★639.0 GBQ6_K877.2 GBBF162121.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Kimi K2.6 on a Mac?
Kimi K2.6 requires at least 295.0 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Kimi K2.6 locally?
Yes — Kimi K2.6 can run locally on consumer hardware. At Q4_K_M quantization it needs 639.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of Kimi K2.6?
At Q4_K_M, the download is about 635.15 GB. The full-precision BF16 version is 2117.18 GB. The smallest option (IQ2_XXS) is 291.11 GB.
- Which GPUs can run Kimi K2.6?
No single consumer GPU has enough VRAM to run Kimi K2.6 at Q4_K_M (639.0 GB). Multi-GPU or professional hardware is required.
- Which devices can run Kimi K2.6?
2 devices with unified memory can run Kimi K2.6 at Q4_K_M (639.0 GB), including NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.