For an instant local deployment, running a pre-configured shell script is ideal.

Carefully read and apply the steps described below.

The engine will automatically fetch large dependencies in the background.

Without any user input, the software calibrates parameters for optimal hardware usage.

📦 Hash-sum → 37cf0c5ecc534c1f1fa5df8a97230104 | 📌 Updated on 2026-07-03 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 48 GB needed to prevent memory swapping to disk Disk Space: 100 GB for multi-modal model vision components Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

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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