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medgemma-27b-it

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medgemma-27b-it

Docker offers the quickest path to setting up this model locally.

Follow the step-by-step instructions below.

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

You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.

🔗 SHA sum: 2b4bcf4c36c314d2aadcde1390d7fe6c | Updated: 2026-06-27
  • CPU: modern architecture (Zen 3 / Alder Lake minimum)
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk Space: at least 100 GB for multiple local LLM variants
  • Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

The **medgemma-27b-it** model is a 27‑billion parameter language model specifically fine‑tuned for medical and clinical applications. It leverages Google’s Gemini architecture combined with specialized medical tokenizations to understand complex terminology and context. The model has been instruction‑tuned on a curated dataset of clinical notes, research papers, and diagnostic guidelines, enabling it to generate accurate and concise medical summaries. In benchmark evaluations, **medgemma-27b-it** achieves state‑of‑the‑art performance on question answering, entity extraction, and dosage recommendation tasks while maintaining a low latency inference profile. Its flexible context window and robust reasoning capabilities make it a valuable tool for healthcare professionals seeking reliable AI assistance at the point of care. The model is available through major cloud platforms and can be integrated into existing EHR systems via standardized APIs.

Parameters 27 B
Context Length 8K tokens
Training Focus Medical & clinical text
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