Deploying this model locally is quickest when done via Docker.
Make sure to follow the instructions below.
The loader auto-caches the model archive (several GBs included).
Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency.
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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