DeepSeek-V3.2 Locally via Ollama 2

Deploying this model locally is quickest when done via a simple curl command.

Proceed by following the technical instructions below.

The script takes care of fetching the multi-gigabyte model weights.

The configuration wizard runs silently to set up the model for peak performance.

📡 Hash Check: cdbd80aff3b3973a6c9d7ed54d8fe41d | 📅 Last Update: 2026-06-25



  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk Space: required: fast PCIe 4.0 drive for instant boots
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

The DeepSeek-V3.2 model sets a new benchmark in large language models with its massive 685 billion parameters and an extended 8K context window. It leverages an innovative mixture‑of‑experts architecture that dynamically routes queries to specialized sub‑networks, delivering both high accuracy and rapid inference. Compared to its predecessor, the model exhibits a 30% reduction in computational overhead while maintaining comparable performance on benchmark suites. The accompanying technical specifications are summarized in the table below, highlighting key metrics such as training data volume and inference latency. Its multimodal capabilities enable seamless integration with text, code, and image inputs, making it a versatile tool for developers and enterprises seeking state‑of‑the‑art AI solutions.

Parameters 685 B
Context Length 8K tokens
Training Data 2.5T tokens
Inference Latency <50 ms
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