Kimi K2 Thinking — Hardware Requirements & GPU Compatibility
Chat
Kimi K2 Thinking is a 1026.4B-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 619.73 GB of VRAM — see which GPUs and Macs can run it below.
46.1K downloads
1.7K likesSpecifications
- Publisher
- Moonshot AI
- Family
- Kimi K2
- Parameters
- 1026.4B
- Architecture
- DeepseekV3ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 163,840
- Release Date
- 2025-11-04
- License
- Other
Get Started
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 | 440.1 GB | 895.0 GB | 436.22 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 452.9 GB | 907.8 GB | 449.05 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 504.3 GB | 959.2 GB | 500.37 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 517.1 GB | 972.0 GB | 513.20 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 619.7 GB | 1074.6 GB | 615.84 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 735.2 GB | 1190.1 GB | 731.32 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 850.7 GB | 1305.6 GB | 846.79 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1030.3 GB | 1485.2 GB | 1026.41 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Kimi K2 Thinking?
Q4_K_M · 619.7 GBKimi K2 Thinking (Q4_K_M) requires 619.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 806+ GB is recommended. Using the full 262K context window can add up to 454.9 GB, bringing total usage to 1074.6 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Kimi K2 Thinking?
Q4_K_M · 619.7 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 619.7 GB of VRAM at Q4_K_M, or 2056.7 GB at BF16. Full 262K context adds up to 454.9 GB (1074.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1026.4B × 4.8 bits ÷ 8 = 615.8 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_M619.7 GBQ4_K_M + full context1074.6 GB- Can NVIDIA GeForce RTX 5090 run Kimi K2 Thinking?
No — Kimi K2 Thinking requires at least 286.1 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 (619.7 GB) offers the best balance of quality and VRAM usage. Q5_K_S (709.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 286.1 GB.
VRAM requirement by quantization
IQ2_XXS286.1 GBIQ3_XS427.3 GBQ3_K_L529.9 GBQ4_K_M ★619.7 GBQ5_K_S709.5 GBBF162056.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Kimi K2 Thinking on a Mac?
Kimi K2 Thinking requires at least 286.1 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 619.7 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 615.84 GB. The full-precision BF16 version is 2052.82 GB. The smallest option (IQ2_XXS) is 282.26 GB.
- Which GPUs can run Kimi K2 Thinking?
No single consumer GPU has enough VRAM to run Kimi K2 Thinking at Q4_K_M (619.7 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 (619.7 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.