Setup DeepSeek-V4-Pro Locally via LM Studio No Python Required

Setup DeepSeek-V4-Pro Locally via LM Studio No Python Required

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

Make sure to follow the instructions below.

The client handles the setup, pulling gigabytes of data automatically.

There is no manual tuning required; the builder will automatically deploy the best matching configuration.

🧾 Hash-sum — af11f86c676296cf5f48280d3eca3ead • 🗓 Updated on: 2026-06-22



  • Processor: high single-core performance needed for token latency
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk Space: required: fast PCIe 4.0 drive for instant boots
  • Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

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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