Homebrew offers the quickest path to setting up this model locally.
Proceed by following the technical instructions below.
Be patient as the system self-retrieves massive model weights dynamically.
During setup, the script automatically determines and applies the best settings.
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:
| Metric | Value |
|---|---|
| Parameters | 1.5 T |
| Training Tokens | 5 T |
| Context Length | 8K |
| FLOPs per Token | 2.3×10^12 |
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