MedGemma 1.5 4B

Overview

Google's v1.5 update to MedGemma — a 4B vision-and-text model fine-tuned on clinical literature, radiology imagery, and medical reports. 128k context, Gemma license.

When to pick this model

• Upgrading existing MedGemma 1.0 deployments without re-architecting
• Drafting and summarizing clinical reports with image grounding
• Research workflows in radiology and medical imaging
• On-prem clinical assistants where API calls aren't an option

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	2.3 GB
Q5_K_M	2.8 GB
Q8_0	4.3 GB
FP16 (no quantization)	8 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Strengths

• Iterative refinement over MedGemma 1.0 with the same footprint
• Compact 4B (~2.3GB VRAM at Q4)
• Multimodal — text plus medical imagery
• 128k context for long patient histories and literature

Limitations

• Decision-support tool only — not for direct clinical use
• Narrow medical focus, weak general performance
• Gated on Hugging Face

Architecture & training

Architecture: Gemma · 4B parameters · multimodal text + image · 128k context

Training: v1.5 iteration of Google's medical fine-tuning on Gemma: clinical literature, radiological imaging, reports.

Quick start

ollama run medgemma1.5

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.