Setup DeepSeek-R1-0528-NVFP4-v2 Quantized GGUF

Setup DeepSeek-R1-0528-NVFP4-v2 Quantized GGUF

The fastest tactical way to launch this model locally is via a Docker image.

Follow the step-by-step instructions below.

Hands-free setup: the system self-downloads the heavy model files.

The script runs a quick hardware check to dynamically adjust parameters for elite speed.

🔐 Hash sum: 92892ed19d905e6551e248ab4e408bc6 | 📅 Last update: 2026-06-25
  • CPU: multi-threading optimized for fast prompt processing
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Disk: high-speed SSD 120 GB to cache model layers
  • Graphics: 12 GB VRAM minimum required for basic quantization

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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