Minimum VRAM
1956GB
FP16 (full model) • Q4 option ≈ 489GB
Best Performance
Collecting data
Benchmark incoming
Most Affordable
Retail data pending
Waiting for retailers
Full-model (FP16) requirements are shown by default. Quantized builds like Q4 trade accuracy for lower VRAM usage.
Filter by quantization, price, and VRAM to compare performance estimates.
We haven’t published GPU benchmarks for this model yet, but you can still plan a stable build:
Hardware requirements and model sizes at a glance.
| Component | Minimum | Recommended | Optimal |
|---|---|---|---|
| VRAM | 489GB (Q4) | 978GB (Q8) | 1956GB (FP16) |
| RAM | 32GB | 64GB | 64GB |
| Disk | 50GB | 100GB | - |
| Model size | 489GB (Q4) | 978GB (Q8) | 1956GB (FP16) |
| CPU | Modern CPU (Ryzen 5/Intel i5 or better) | Modern CPU (Ryzen 5/Intel i5 or better) | Modern CPU (Ryzen 5/Intel i5 or better) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
Common questions about running moonshotai/Kimi-K2-Thinking locally
Kimi K2 Thinking prioritizes reasoning accuracy over raw speed, so this guide focuses on the VRAM, bandwidth, and quantization choices that keep its deliberate outputs stable on local GPUs.
This model loads the full parameter set into memory, but only a subset of experts activate per token. Plan VRAM for the complete 1,000,000,000,000 (1T) footprint while expecting throughput similar to a 32B dense model. Configuration: 384 experts • 8 active per token.
Use runtimes like llama.cpp, text-generation-webui, or vLLM. Download the quantized weights from Hugging Face, ensure you have enough VRAM for your target quantization, and launch with GPU acceleration (CUDA/ROCm/Metal).
Start with Q4 for wide GPU compatibility. Upgrade to Q8 if you have spare VRAM and want extra quality. FP16 delivers the highest fidelity but demands workstation or multi-GPU setups.
Q4_K_M and Q5_K_M are GGUF quantization formats that balance quality and VRAM usage. Q4_K_M uses ~489GB VRAM with good quality retention. Q5_K_M uses slightly more VRAM but preserves more model accuracy. Q8 (~978GB) offers near-FP16 quality. Standard Q4 is the most memory-efficient option for moonshotai/Kimi-K2-Thinking.
Official weights are available via Hugging Face. Quantized builds (Q4, Q8) can be loaded into runtimes like llama.cpp, text-generation-webui, or vLLM. Always verify the publisher before downloading.