DeepSeek-V4-Pro PC with NPU

The most rapid route to a local installation of this model is through WSL2.

Check out the detailed setup guide below to begin.

The process automatically pulls down gigabytes of critical model assets.

The deployment tool scans your environment and chooses the ideal parameters.

🧩 Hash sum → 4833f89cbf9a9f8d992430885a1fd616 — Update date: 2026-07-04 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 48 GB needed to prevent memory swapping to disk Disk: 150+ GB for high-context vector database storage Graphics: TensorRT-LLM / vLLM inference engine compatible chip

DeepSeek-V4-Pro introduces a groundbreaking sparse‑attention architecture that dramatically cuts compute costs while retaining the ability to model long‑range contexts. With a staggering parameter count exceeding 1.5 trillion weights, the model delivers superior multilingual capabilities and nuanced reasoning. It has been trained on a meticulously curated training dataset of more than 5 trillion tokens, encompassing code repositories, scientific papers, and diverse conversational sources. Benchmark results highlight its state‑of‑the‑art performance across reasoning, coding, and factual QA tasks, often outpacing earlier models by double‑digit margins. Key technical specifications are summarized below:

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