A standalone PowerShell module provides the fastest route to local installation.
Follow the step-by-step instructions below.
An automated background process downloads all required large-scale files.
The deployment tool scans your environment and chooses the ideal parameters.
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鈥憃f鈥憈he鈥慳rt 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鈥慻rade 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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