Medgemma 1.5 4B IT — Hardware Requirements & GPU Compatibility
Vision
MedGemma 1.5 4B is Google's 4.3-billion-parameter instruction-tuned vision-language model in the MedGemma line of health-AI foundation models, succeeding the original MedGemma 4B at the same size. It is built for developers creating healthcare applications, covering tasks such as medical image interpretation and clinical text understanding, and is not a validated diagnostic tool: outputs need independent verification and further evaluation before any clinical use. At this size it runs on a single consumer GPU once quantized. It is released under Google's Health AI Developer Foundations terms, a use-restricted license that requires accepting specific health-AI conditions on Hugging Face rather than a fully open one. Published in January 2026, it is the second 4B release in the MedGemma series.
326.4K downloads
920 likesSpecifications
- Publisher
- Family
- Gemma
- Parameters
- 4.3B
- Release Date
- 2026-01-07
- License
- Other
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HuggingFace
How Much VRAM Does Medgemma 1.5 4B IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.0 GB | — | 1.83 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.1 GB | — | 1.88 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.3 GB | — | 2.10 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.4 GB | — | 2.15 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 2.8 GB | — | 2.58 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.4 GB | — | 3.06 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.9 GB | — | 3.55 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.7 GB | — | 4.30 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Medgemma 1.5 4B IT?
Q4_K_M · 2.8 GBMedgemma 1.5 4B IT (Q4_K_M) requires 2.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Medgemma 1.5 4B IT?
Q4_K_M · 2.8 GB59 devices with unified memory can run Medgemma 1.5 4B IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Medgemma 1.5 4B IT
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Medgemma 1.5 4B IT need?
Medgemma 1.5 4B IT requires 2.8 GB of VRAM at Q4_K_M, or 9.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 4.3B × 4.8 bits ÷ 8 = 2.6 GB
KV Cache + Overhead ≈ 0.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M2.8 GB- What's the best quantization for Medgemma 1.5 4B IT?
For Medgemma 1.5 4B IT, Q4_K_M (2.8 GB) offers the best balance of quality and VRAM usage. Q5_K_S (3.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.3 GB.
VRAM requirement by quantization
IQ2_XXS1.3 GBIQ3_XS1.9 GBQ4_02.4 GBIQ4_NL2.7 GBQ4_K_M ★2.8 GBBF169.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Medgemma 1.5 4B IT on a Mac?
Medgemma 1.5 4B IT requires at least 1.3 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Medgemma 1.5 4B IT locally?
Yes — Medgemma 1.5 4B IT can run locally on consumer hardware. At Q4_K_M quantization it needs 2.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Medgemma 1.5 4B IT?
At Q4_K_M, Medgemma 1.5 4B IT can reach ~1690 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~231 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 2.8 × 0.65 = ~1831 tok/s
Estimated speed at Q4_K_M (2.8 GB)
~1831 tok/s~231 tok/s~1831 tok/s~1690 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Medgemma 1.5 4B IT?
At Q4_K_M, the download is about 2.58 GB. The full-precision BF16 version is 8.60 GB. The smallest option (IQ2_XXS) is 1.18 GB.
- Which GPUs can run Medgemma 1.5 4B IT?
52 consumer GPUs can run Medgemma 1.5 4B IT at Q4_K_M (2.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Medgemma 1.5 4B IT?
59 devices with unified memory can run Medgemma 1.5 4B IT at Q4_K_M (2.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.