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 IQ2_XXS it needs about 294.99 GB of VRAM — see which GPUs and Macs can run it below.
3.1M downloads
1.4K likesSpecifications
- Publisher
- Moonshot AI
- Family
- Kimi K2
- Parameters
- 1058.6B
- Architecture
- KimiK25ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 163,840
- License
- Other
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HuggingFace
How Much VRAM Does Kimi K2.6 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 295.0 GB | 749.9 GB | 291.11 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_S | 2.50 | 334.7 GB | 789.6 GB | 330.81 GB | Importance-weighted 2-bit, small |
| Q2_K | 3.40 | 453.8 GB | 908.7 GB | 449.90 GB | 2-bit quantization with K-quant improvements |
| IQ3_S | 3.40 | 453.8 GB | 908.7 GB | 449.90 GB | Importance-weighted 3-bit, small |
| Q3_K_L | 4.10 | 546.4 GB | 1001.3 GB | 542.53 GB | 3-bit large quantization |
| Q8_0 | 8.00 | 1062.5 GB | 1517.4 GB | 1058.59 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Kimi K2.6?
Q3_K_L · 546.4 GBKimi K2.6 (Q3_K_L) requires 546.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 711+ GB is recommended. Using the full 262K context window can add up to 454.9 GB, bringing total usage to 1001.3 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Kimi K2.6?
Q3_K_L · 546.4 GB2 devices with unified memory can run Kimi K2.6, including NVIDIA DGX H100.
Decent
— Enough memory, may be tightBenchmarks
View all 8 →Related Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Kimi K2.6 need?
Kimi K2.6 requires 295.0 GB of VRAM at IQ2_XXS, or 1062.5 GB at Q8_0. Full 262K context adds up to 454.9 GB (749.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1058.6B × 2.2 bits ÷ 8 = 291.1 GB
KV Cache + Overhead ≈ 3.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 458.8 GB (at full 262K context)
VRAM usage by quantization
IQ2_XXS295.0 GBIQ2_XXS + full context749.9 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, IQ3_S (453.8 GB) offers the best balance of quality and VRAM usage. Q3_K_L (546.4 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 GBIQ2_S334.7 GBQ2_K453.8 GBIQ3_S ★453.8 GBQ3_K_L546.4 GBQ8_01062.5 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 IQ2_XXS quantization it needs 295.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of Kimi K2.6?
At IQ2_XXS, the download is about 291.11 GB. The full-precision Q8_0 version is 1058.59 GB.
- Which GPUs can run Kimi K2.6?
No single consumer GPU has enough VRAM to run Kimi K2.6 at IQ2_XXS (295.0 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 IQ2_XXS (295.0 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.