If you want the fastest local installation for this model, use Docker.

Refer to the instructions below to proceed.

The loader auto-caches the model archive (several GBs included).

During setup, the script automatically determines and applies the best settings tailored to your machine.

📎 HASH: 2fa1ea3a6df6a8f77e8fc0e0e960589d | Updated: 2026-06-28 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: required: 16 GB absolute minimum for small models Storage:100 GB free space for HuggingFace cache folder Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

DeepSeek-R1-0528-NVFP4-v2 is a large language model optimized for low‑precision inference on NVIDIA’s Hopper architecture. It leverages NVFP4 data type to achieve higher throughput while maintaining state‑of‑the‑art accuracy. The model features a parameter count of 180 B and was trained on over 5 trillion tokens, enabling robust reasoning across diverse domains. Its inference latency averages 23 ms per token on a single A100‑80GB, making it suitable for real‑time applications. The design incorporates mixture‑of‑experts layers that dynamically route queries to specialized subnetworks, improving both efficiency and scalability. Below is a quick comparison of key technical specifications:

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