Deploy Kimi-K2.5-NVFP4 Windows 10 with Native FP4 Offline Setup

The shortest path to running this model is by activating Hyper-V features.

Proceed by following the technical instructions below.

The setup auto-downloads all needed files (several GBs).

The deployment tool scans your environment and chooses the ideal parameters.

🧾 Hash-sum — effac88e87f505df80ca2de4021e8cbb • 🗓 Updated on: 2026-06-30 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 32 GB or higher for smooth 32k context lengths Disk: high-speed SSD 120 GB to cache model layers Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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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