Run DeepSeek-R1-0528-NVFP4-v2 with Native FP4 For Beginners

Docker offers the quickest path to setting up this model locally.

Follow the sequence of steps detailed below.

The setup auto-streams the model assets (expect a multi-GB download).

The automated installation script takes care of everything by tailoring the setup perfectly to your system specs.

🗂 Hash: 3b63b1e8c4b16f0ee80b3e59c2b3e6c1 • Last Updated: 2026-06-27 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: required: 16 GB absolute minimum for small models Disk: 150+ GB for high-context vector database storage 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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