Kimi-K2.5-NVFP4

Running this model locally is fastest when deployed through Docker.

Use the instructions provided below to complete the setup.

During setup, the script automatically determines and applies the best settings tailored to your machine.

📎 HASH: 326291fee61eac9ba6e8fafb231ae330 | Updated: 2026-06-25 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: minimum 16 GB for stable 8B model loading Disk Space:70 GB free space for full FP16 weights storage Graphics: 12 GB VRAM minimum required for basic quantization

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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