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Kimi-K2.5-NVFP4 on Your PC Direct EXE Setup

Running this model locally is fastest when deployed through Docker.

Review and follow the instructions below.

1-click setup: the app automatically fetches the large weight files.

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

📊 File Hash: d70c0d9e338a788f7c6696cb339ba57d — Last update: 2026-06-27
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  • CPU: multi-threading optimized for fast prompt processing
  • RAM: minimum 16 GB for stable 8B model loading
  • Disk: 150+ GB for high-context vector database storage
  • Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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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