DeepSeek-V4-Pro 100% Private PC with 1M Context Windows

If you need a near-instant local setup, just fetch files via a basic curl request.

Go through the configuration rules shown below.

The download manager will automatically pull several gigabytes of data.

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

šŸ” Hash sum: 7261dc6276b7b46a3e50be3130f38fc2 | šŸ“… Last update: 2026-06-25



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: at least 32 GB in dual-channel mode for bandwidth
  • Disk Space: at least 100 GB for multiple local LLM variants
  • 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 2.3Ɨ10^12
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