Deploy DeepSeek-V4-Pro Using Pinokio

Deploying locally takes the least amount of time when executed through native OS tools.

Please adhere to the deployment steps listed below.

Hands-free setup: the system self-downloads the heavy model files.

The program scans your VRAM and RAM to seamlessly apply optimal configurations.

🗂 Hash: d09dcc22c14793a884ecc1e5868e1671 • Last Updated: 2026-06-30
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  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 32 GB highly recommended for 26B+ GGUF models
  • Disk Space: 100 GB for multi-modal model vision components
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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
  1. Downloader pulling advanced upscaler model weights like SUPIR-v2 for Forge WebUI
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