Quick Run Kimi-K2.5-NVFP4 Locally via Ollama 2

For the fastest local setup of this model, Docker is the best choice.

Follow the guidelines below to continue.

The client handles the setup, pulling gigabytes of data automatically.

There is no manual tuning required; the builder will automatically deploy the best matching configuration.

🔧 Digest: ad67de087d5a5d419e5b9315463eeec0 • 🕒 Updated: 2026-06-23 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 48 GB needed to prevent memory swapping to disk Disk Space:70 GB free space for full FP16 weights 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.

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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