Kimi K2 Thinking — Hardware Requirements & GPU Compatibility
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Kimi K2 Thinking is a 1058.1B-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 638.75 GB of VRAM — see which GPUs and Macs can run it below.
86.9K downloads
1.7K likesSpecifications
- Publisher
- Moonshot AI
- Family
- Kimi K2
- Parameters
- 1058.1B
- Architecture
- DeepseekV3ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 163,840
- Release Date
- 2025-11-04
- License
- Other
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HuggingFace
How Much VRAM Does Kimi K2 Thinking Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 453.6 GB | 908.5 GB | 449.70 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 466.8 GB | 921.7 GB | 462.93 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 519.7 GB | 974.6 GB | 515.83 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 532.9 GB | 987.9 GB | 529.06 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 638.8 GB | 1093.7 GB | 634.87 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 757.8 GB | 1212.7 GB | 753.91 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 876.8 GB | 1331.7 GB | 872.95 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1062 GB | 1516.9 GB | 1058.12 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Kimi K2 Thinking?
Q4_K_M · 638.8 GBKimi K2 Thinking (Q4_K_M) requires 638.8 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 1093.7 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Kimi K2 Thinking?
Q4_K_M · 638.8 GB2 devices with unified memory can run Kimi K2 Thinking, including NVIDIA DGX H100.
Decent
— Enough memory, may be tightWhere to Download Kimi K2 Thinking
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 Thinking need?
Kimi K2 Thinking requires 638.8 GB of VRAM at Q4_K_M, or 2120.1 GB at BF16. Full 262K context adds up to 454.9 GB (1093.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1058.1B × 4.8 bits ÷ 8 = 634.9 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
Q4_K_M638.8 GBQ4_K_M + full context1093.7 GB- Can NVIDIA GeForce RTX 5090 run Kimi K2 Thinking?
No — Kimi K2 Thinking requires at least 294.9 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Kimi K2 Thinking?
For Kimi K2 Thinking, Q4_K_M (638.8 GB) offers the best balance of quality and VRAM usage. Q5_K_S (731.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 294.9 GB.
VRAM requirement by quantization
IQ2_XXS294.9 GBIQ3_XS440.4 GBQ3_K_L546.2 GBQ4_K_M ★638.8 GBQ5_K_S731.3 GBBF162120.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Kimi K2 Thinking on a Mac?
Kimi K2 Thinking requires at least 294.9 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 Thinking locally?
Yes — Kimi K2 Thinking can run locally on consumer hardware. At Q4_K_M quantization it needs 638.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of Kimi K2 Thinking?
At Q4_K_M, the download is about 634.87 GB. The full-precision BF16 version is 2116.24 GB. The smallest option (IQ2_XXS) is 290.98 GB.
- Which GPUs can run Kimi K2 Thinking?
No single consumer GPU has enough VRAM to run Kimi K2 Thinking at Q4_K_M (638.8 GB). Multi-GPU or professional hardware is required.
- Which devices can run Kimi K2 Thinking?
2 devices with unified memory can run Kimi K2 Thinking at Q4_K_M (638.8 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.