DeepSeek-R1-0528-NVFP4-v2 Zero Config Dummy Proof Guide

For the fastest local setup of this model, enabling Windows Features is best.

Follow the guidelines below to continue.

Everything happens automatically, including the heavy cloud asset download.

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

🧾 Hash-sum — 98e753363177bdc827ecb6ddc60be77a • 🗓 Updated on: 2026-06-30 Verify Processor: high single-core performance needed for token latency RAM: 48 GB needed to prevent memory swapping to disk Disk: 150+ GB for high-context vector database storage Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

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:

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