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How to Run DeepSeek-R1-0528-NVFP4-v2 on AMD/Nvidia GPU Step-by-Step

07/03/2026 · Inksilopedia

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How to Run DeepSeek-R1-0528-NVFP4-v2 on AMD/Nvidia GPU Step-by-Step

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

Follow the guidelines below to continue.

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

The setup file includes a feature that instantly optimizes all configurations.

📘 Build Hash: d18c6fc7b68651073d3a17ef3a59628c • 🗓 2026-06-26
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  • Processor: next-gen chip for heavy context processing
  • 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:

Parameter Count 180 B
Training Tokens 5 trillion
Inference Latency 23 ms/token
Precision NVFP4
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  7. Setup tool configuring MemGPT memory structures alongside persistent local GGUF nodes
  8. DeepSeek-R1-0528-NVFP4-v2 Zero Config Direct EXE Setup FREE

https://engenera.org/category/embeddings/