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

Follow the step-by-step instructions below.

Be patient as the system self-retrieves massive model weights dynamically.

There is no manual tuning required; the builder deploys the best matching configuration.

📤 Release Hash: a9869848da55fd5ec4aca16c6db1b575 • 📅 Date: 2026-06-29 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: required: fast PCIe 4.0 drive for instant boots 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:

Metric	Value
Parameters	1.5 T
Training Tokens	5 T
Context Length	8K
FLOPs per Token	2.3×10^12

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