Deploy DeepSeek-R1-0528-NVFP4-v2 on AMD/Nvidia GPU with 1M Context

🔒 Hash checksum: 04f37718d43e1d3ada4e5a6e653f5f0e • 📆 Last updated: 2026-07-23 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: at least 32 GB in dual-channel mode for bandwidth Disk: 150+ GB for high-context vector database storage Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

Unlocking the Potential of DeepSeek-R1-0528-NVFP4-v2

DeepSeek-R1-0528-NVFP4-v2, a cutting-edge large language model, is specifically designed for low-precision inference on NVIDIA’s Hopper architecture. By harnessing the power of NVFP4 data type, this model achieves an impressive balance between throughput and state-of-the-art accuracy.

With a parameter count of 180 B, this model has undergone extensive training on over 5 trillion tokens, allowing it to excel in diverse domains and provide robust reasoning capabilities. Its inference latency averages 23 ms per token on a single A100-80GB, making it an ideal choice for real-time applications.

Technical Specifications

1. Parameter Count: 180 B
2. Training Tokens: 5 trillion
3. Inference Latency: 23 ms/token
4. Precision: NVFP4

Design Overview

• The model’s design incorporates mixture-of-experts layers, which dynamically route queries to specialized subnetworks. This approach improves both efficiency and scalability.
• The use of NVFP4 data type enables the model to achieve higher throughput while maintaining state-of-the-art accuracy.

Comparison of Key Technical Specifications

Unlocking the Power of DeepSeek-R1-0528-NVFP4-v2

By leveraging its cutting-edge architecture and extensive training data, DeepSeek-R1-0528-NVFP4-v2 is poised to revolutionize various applications, from natural language processing to expert systems. With its impressive performance capabilities and optimized design, this model offers unparalleled flexibility and scalability for developers seeking to build innovative solutions.

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