Quick Run DeepSeek-V4-Pro Using Pinokio No Python Required

If you want the fastest local installation for this model, use standard pip packages.

Make sure to follow the instructions below.

The system automatically triggers a cloud download for all heavy weights.

The script runs a quick hardware check to dynamically adjust parameters for elite speed.

💾 File hash: e2996cdeb7e5c87f2025191d07f31796 (Update date: 2026-06-29)
  • CPU: modern architecture (Zen 3 / Alder Lake minimum)
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • 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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