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Install Kimi-K2.5-NVFP4 Windows 11 No-Code Guide
To install this model locally in the shortest time, opt for Docker.
Simply follow the directions outlined below.
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The loader auto-caches the model archive (several GBs included).
You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.
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.
| Training Data Size | 1.5 TB |
|---|---|
| Parameter Count | 7B |
| Inference Latency (ms) | 12 |
| GPU Memory (GB) | 16 |
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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