Setup DeepSeek-R1-0528-NVFP4-v2 Locally (No Cloud) Quantized GGUF

The fastest method for installing this model locally is by using Docker.

Simply follow the directions outlined below.

The installer automatically pulls the model (could be multiple GBs).

The program scans your VRAM and RAM to seamlessly apply optimal configurations.

🧮 Hash-code: 113af2262da7e86bf9e6e376839225a5 • 📆 2026-06-29
  • Processor: 6-core 3.5 GHz minimum required
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

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
  1. Setup tool refining CPU thread binding boundaries for maximized llama.cpp performance
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  5. Setup tool initializing prefix-caching parameters inside production-tier vLLM clusters
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  9. Setup tool mapping local CUDA environment variables for native nvcc code compilation
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