Quick Run Kimi-K2.5-NVFP4 PC with NPU with Native FP4

For the fastest local setup of this model, Docker is the best choice.

Simply follow the directions outlined below.

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The installer automatically pulls the model (could be multiple GBs).

The installer will automatically analyze your hardware and select the optimal configuration for your system.

🔐 Hash sum: 5f0d2230803e8b4fa497abc9ed75dfc1 | 📅 Last update: 2026-06-24 Verify Processor: high single-core performance needed for token latency RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space:70 GB free space for full FP16 weights storage Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

The Kimi-K2.5-NVFP4 model introduces a breakthrough in efficient inference for large language tasks. Built on a sparse-attention architecture, it reduces computational load while preserving high contextual understanding. The model achieves state‑of‑the‑art performance on benchmarks such as MMLU and TriviaQA, often outperforming larger parameter counterparts. Its parameter count and memory footprint are optimized for deployment on consumer‑grade hardware, as illustrated in the comparison table below.

The following table provides key metrics including training data size, inference latency, and GPU memory usage, enabling developers to assess suitability for their applications.

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