Quantizations
How to Run Kimi-K2.5-NVFP4 with Native FP4
To get this model running locally in no time, utilize the built-in WSL tools.
Execute the commands and steps outlined below.
The download manager will automatically pull several gigabytes of data.
The installer diagnoses your environment to deploy the most compatible profile.
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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