Run DeepSeek-V4-Flash Locally via Ollama 2 with 1M Context

Deploying this model locally is quickest when done via Docker.

Please follow the instructions listed below to get started.

No manual effort needed; the setup auto-ingests the large data.

The smart installation system will instantly find the perfect configuration for your specific hardware.

📦 Hash-sum → f49d599f523eb692aca9b8fde6cca225 | 📌 Updated on 2026-06-24



  • Processor: high single-core performance needed for token latency
  • RAM: high-speed DDR5 memory preferred for CPU offloading
  • Disk: high-speed SSD 120 GB to cache model layers
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

The **DeepSeek-V4-Flash** model delivers state-of-the-art performance across a wide range of natural language tasks. It leverages an optimized transformer architecture with sparse attention mechanisms, enabling faster inference while maintaining high accuracy. The model supports a context window of up to **128K tokens**, allowing it to understand and generate long-form content with contextual coherence. In benchmarks, it outperforms previous generation models by an average of **7%** on reasoning tasks and **5%** on multilingual generation. Below is a concise comparison of its key technical specifications versus the preceding DeepSeek-V3 model.

Parameters 180B 150B
Context Length 128K tokens 64K tokens
Training Data 2.5T tokens 1.8T tokens

This combination of efficiency and capability makes **DeepSeek-V4-Flash** a compelling choice for developers seeking real-time AI solutions.

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