Quick Run DeepSeek-R1-0528-NVFP4-v2 Quantized GGUF

Quick Run DeepSeek-R1-0528-NVFP4-v2 Quantized GGUF

Running this model locally is fastest when deployed through Docker.

Just follow the guidelines provided below.

The system automatically triggers a cloud download for all heavy weights.

The smart installation system will instantly find the perfect configuration for your specific hardware.

📊 File Hash: e2b15496ff6d98a7b7753f77b40e1fbe — Last update: 2026-06-26



  • Processor: high single-core performance needed for token latency
  • RAM: enough space for background apps and OS overhead
  • Disk Space: 100 GB for multi-modal model vision components
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

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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