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LatestGoogle DeepMind1.5
Google DeepMindVision-Language

Google's MedGemma brings open vision AI to medicine

The new 4-billion-parameter vision-language model is specialized for tasks in radiology, pathology, and complex clinical reasoning.

Jan 7, 2026
NotableGemma
MedGemma 1.5 4B IT

Google DeepMind has released MedGemma 1.5, a new open-weights model designed specifically for the medical domain. This 4-billion-parameter model extends the capabilities of the Gemma family into specialized areas, focusing on vision-language tasks that are critical for healthcare.

As a vision-language model (VLM), MedGemma is trained to understand and reason about both text and images. According to its official model card, its instruction tuning targets complex use cases like interpreting radiological scans, analyzing pathology slides, and aiding in multifaceted clinical reasoning.

The release marks a significant step for open-source AI in healthcare. By providing researchers and developers with a capable, specialized foundation model, MedGemma could accelerate the development of new diagnostic aids, medical education tools, and clinical support systems that were previously reliant on proprietary, closed-source models.

MedGemma 1.5 is available under the Gemma license, which permits commercial use and distribution subject to its terms. This particular version, medgemma-1.5-4b-it, is an instruction-tuned variant, making it ready for conversational and question-answering applications out of the box.

Sources

  • google/medgemma-1.5-4b-it

    Hugging Face

    Visit
OlderSupertone Open-Sources Supertonic 2 Voice ModelSupertone · Text → Speech · 10 months agoNewerZhipu AI Releases Open, Bilingual GLM-Image ModelZhipu AI · Text → Image · 10 months ago

Get the model

Hugging Face

Specs

Parameters4B
Context window131K tokens
Size8.6 GB
PrecisionBF16
ArchitectureGemma3ForConditionalGeneration
LicenseGEMMA
Downloads179.1K
Likes1.1K

Can you run it?

Runs on a laptop — about 4.1 GB at 4-bit.

  • BF16 (as published)

    12 GB GPU (RTX 3060 / 4070) · 16 GB Mac

    10.4 GB
  • 8-bit

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    5.9 GB
  • 4-bit

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    4.1 GB
Your machine
GGUF builds
  • KV cache approximated from model size (no config metadata) at 8,192 tokens.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Vision-LanguageReasoning

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