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Model reference · open weights

medgemma-pt

LLMs google Vision + text 1 build Its own licence terms 1k dl/mo

medgemma-pt is an open-weight language model from Google. medgemma-4b-pt (BF16) weighs 8.6 GB; the smallest configuration that runs it is RTX 3060 12 GB.

  • Medgemma-pt is a 4.3B parameter multimodal model developed by Google for medical text and image comprehension.
  • It supports image-text-to-text tasks with a context length of at least 128K tokens and uses a decoder-only transformer architecture.
  • The model is governed by the Health AI Developer Foundations terms of use.

Summary of the google/medgemma-4b-pt model card, 2026-10-01

Hugging Face ↗GitHub ↗Paper ↗

What it is

Released byGoogle
Released2025-05-19
Parameters4.3B
VRAM8.6 GB for the weights

What it runs on

Memory and cards for medgemma-4b-pt (BF16)

8.6 GBweights, file size
762 MBruntime overhead, at least

How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.

CardWeights alone
RTX 3060 12 GBfits
RTX 4060 Ti 16 GBfits
RTX 3090 24 GBfits
RTX 4090 24 GBfits
RTX 5090 32 GBfits
L40S 48 GBfits
A100 80 GBfits
H100 80 GBfits
RTX PRO 6000 Blackwell 96 GBfits
DGX Spark (GB10) 128 GB unifiedfits
H200 141 GBfits
B200 180 GBfits

How it works

How language models work

Your prompttext / messagesTransformerattention over tokensNext-token loopgenerate + streamResponsetext · tool callsA language model reads your tokens and predicts the next one, again and again, streaming the reply back.
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