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Kimi-K2.5-NVFP4 on AMD/Nvidia GPU Uncensored Edition

2 de julio de 2026

Kimi-K2.5-NVFP4 on AMD/Nvidia GPU Uncensored Edition

Using a native PowerShell script is the absolute quickest way to install this model.

Use the instructions provided below to complete the setup.

The tool automatically synchronizes and downloads the model database.

To guarantee smooth performance, the process auto-selects the best options.

🔒 Hash checksum: 236c2c86dd7581603707418fe60db2f2 • 📆 Last updated: 2026-06-28
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  • Processor: 6-core 3.5 GHz minimum required
  • RAM: minimum 16 GB for stable 8B model loading
  • Disk: 150+ GB for high-context vector database 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.

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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