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MedGemma 27B Text IT

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MedGemma 27B Text IT is an open-weights language model from Google, released May 2025. Its published size is 27.01B parameters. Licence: health-ai-developer-foundations.

Catalogue identity notes

google/medgemma-27b-text-it @5b667cf (created 2025-05-19, licence health-ai-developer-foundations). Google MedGemma 27B text-only instruction model (May 2025), Gemma 3 based; gated under the Health AI Developer Foundations terms. No ollama library tag (ollama ships the multimodal 27B). Independent rows: BRIDGE and Open PT-LLM.

Model information sources

Release date ↗ · Weights licence ↗

HF safetensors metadata 27,009,002,240 parameters.

57.5±3.8#268 of 949 LLMsfrom 2 benchmark results across 2 sources* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of MedGemma 27B Text IT, out of 1132 with a score, then more from Google.

#modelscorewhereRAMreleased
349Step 2 16K Experimental (2024-12) StepFun58±3comparison only
350xLAM-2-3b-fc-r Salesforce58±4local~3.5GB2025-03
351MedGemma 27B Text IT Google58±4local~18GB2025-05
352GPT-4o (2024-08-06) OpenAI57±1api $4.38/M2024-08
353Gemini 2.0 Flash Thinking Experimental (01-21) Google57±2comparison only2025-01
more from Google
9Gemini 4 Argon (limited preview) Google82±1limited preview2026-09
23Gemini 3.8 Flash Google77±1api $1.50/M2026-09
32Gemini 3.7 Flash Google76±1api $1.50/M2026-08

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests2 not in a task · 94% of its score
testresultvs the others in its rundifficultycountswhy
BRIDGE clinical text, zero-shot average (87 tasks)40.5% ✎▁▁▁▁▁▂▂▃▄▆█▃ beats 78/97hard · avg 32%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 71%blunt · at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)79.5% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 256/276medium · avg 73%▇▇▇▇▇▇▇▇▇ 23%at its level · noisy

$ where to run it

llama.cpp · recommended

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run MedGemma 27B Text IT · needs ~16.55 GB · from huggingface.co/unsloth
    chat in the terminal
    llama cli -hf unsloth/medgemma-27b-text-it-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf unsloth/medgemma-27b-text-it-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run MedGemma 27B Text IT · needs ~16.55 GB · from huggingface.co/unsloth

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/unsloth/medgemma-27b-text-it-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run MedGemma 27B Text IT · needs ~16.55 GB · from huggingface.co/unsloth
    chat in the terminal
    ollama run hf.co/unsloth/medgemma-27b-text-it-GGUF:Q4_K_M

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run MedGemma 27B Text IT · needs ~16 GB, Mac only · from huggingface.co/lmstudio-community
    chat in the terminal
    mlx_lm.chat --model lmstudio-community/medgemma-27b-text-it-MLX-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model lmstudio-community/medgemma-27b-text-it-MLX-4bit

Official weights and documentation: huggingface.co/google ↗

$ how the score was worked out · BRIDGE Medical Leaderboard (H… 71%, Open Portuguese LLM Leaderboa… 23%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
BRIDGE Medical Leaderboard (Harvard Medical School / Brigham and Women's YLab)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 71%MIT
Open Portuguese LLM Leaderboard (archived)1▇▇▇▇▇▇▇ 23%Apache-2.0
prior · pull toward the middle▇▇ 6%
weighted average= 57.5

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json