Setting up this model locally is incredibly fast if you use the native CMD prompt.
Simply follow the directions outlined below.
The loader auto-caches the model archive (several GBs included).
The deployment tool scans your environment and chooses the ideal parameters.
📎 HASH: 33762cd3a1ee1491ad7f24e95ae4af9a | Updated: 2026-06-23
- Processor: 4.0 GHz+ boost clock recommended for CPU inference
- RAM: enough space for background apps and OS overhead
- Disk: 150+ GB for high-context vector database storage
- Graphics: stable 30+ tk/s at 4-bit quantization on medium setup
|
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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