Kimi-K2.5-NVFP4 on Copilot+ PC with Native FP4 Step-by-Step

The fastest method for installing this model locally is by using Docker.

Kindly follow the on-screen instructions below.

An automated background process downloads all required large-scale files.

The configuration wizard runs silently to set up the model for peak performance.

🧾 Hash-sum — eb28287f808de90e36b1e2f75d188780 • 🗓 Updated on: 2026-06-28 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 48 GB needed to prevent memory swapping to disk Disk Space: 100 GB for multi-modal model vision components 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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