DeepSeek-V4-Pro Locally via Ollama 2 Dummy Proof Guide

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DeepSeek-V4-Pro Locally via Ollama 2 Dummy Proof Guide

For the fastest local setup of this model, Docker is the best choice.

Please follow the instructions listed below to get started. The setup auto-downloads all needed files (several GBs).

The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.

🧩 Hash sum → fe7c11bea1e085a797ebb1de40b069d7 — Update date: 2026-06-26



  • CPU: 8-core / 16-thread recommended for orchestration
  • RAM: fast 5600MHz+ required to avoid memory bottlenecks
  • Disk Space: free: 80 GB on system drive for scratch space
  • Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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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