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" alt="Zero-Click Run Kimi-K2.5-NVFP4 via WebGPU (Browser)" style="display:block; width:100%; height:auto; border-radius:8px;">
The fastest method for installing this model locally is by using Docker.
Review and follow the instructions below.
The client handles the setup, pulling gigabytes of data automatically.
The deployment tool scans your environment and chooses the ideal parameters.
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📤 Release Hash: cf4ce0400600de81005c582e7feda408 • 📅 Date: 2026-06-30
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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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