How to Deploy DeepSeek-R1-0528-NVFP4-v2 One-Click Setup

How to Deploy DeepSeek-R1-0528-NVFP4-v2 One-Click Setup

📄 Hash Value: 138d05b5862e294dd3116e49e45b0469 | 📆 Update: 2026-07-20



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

Unlocking the Potential of DeepSeek-R1-0528-NVFP4-v2This cutting-edge language model is specifically designed to excel on NVIDIA’s Hopper architecture, leveraging the power of NVFP4 data type to achieve unparalleled accuracy. By doing so, it offers a significant boost in throughput while maintaining the highest standards of performance. With a parameter count of 180 B and a training dataset spanning over 5 trillion tokens, this model is equipped to tackle even the most complex reasoning tasks across diverse domains.

Technical Specifications 180 B
Training Dataset Size 5 trillion tokens
Inference Latency 23 ms/token
Data Type NVFP4

Future-Proofing with DeepSeek-R1-0528-NVFP4-v2With its exceptional performance and efficiency, this language model is poised to revolutionize the way we approach natural language processing tasks. Its unique architecture and advanced features make it an attractive choice for developers and researchers looking to push the boundaries of AI innovation. By harnessing the power of NVFP4 data type, DeepSeek-R1-0528-NVFP4-v2 offers a compelling solution for applications requiring high-throughput inference and accuracy.

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Our team of experts is dedicated to providing personalized support and guidance to help you get the most out of DeepSeek-R1-0528-NVFP4-v2. Whether you’re looking for custom installation, optimization, or training solutions, we’ve got you covered.

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Don’t miss out on this opportunity to unlock the full potential of your language model. Contact us today to learn more about DeepSeek-R1-0528-NVFP4-v2 and how it can help drive innovation in your field.