Kimi-K2.5-NVFP4

Running this model locally is fastest when deployed through Docker.

Follow the step-by-step instructions below.

To guarantee smooth performance, the installation process auto-selects the best possible options for your PC.

🔐 Hash sum: 6e0c77de6eadb0c9d06994bc056e9bb8 | 📅 Last update: 2026-06-27 Verify CPU: multi-threading optimized for fast prompt processing RAM: 64 GB to avoid OOM crashes on large contexts Disk Space: at least 100 GB for multiple local LLM variants Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

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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