medgemma-4b-it Memory at each precision, the licence in plain words, and where it fits.
Short answer. medgemma-4b-it needs about 4.1 GB of memory at 4-bit, 6.2 GB at 8-bit and 10 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 4.3 billion parameters and a 131,072-token context window. Licence in short: custom licence: check before commercial use. By memory alone, on ANVE’s launch configurations: ANVE One fits at BF16, ANVE Office fits at BF16, ANVE Office Pro (GPU) fits at BF16. These are estimates from its published size, not benchmarks.
01
What it is
medgemma-4b-it is a vision-language model: it reads images (scanned pages, photos, charts and screenshots) as well as text, published by Google on Hugging Face on 19 May 2025. It has about 4.3 billion parameters and a configured context window of 131,072 tokens.
Its card lists the licence as a custom licence (health-ai-developer-foundations). In short, the licence sets its own terms; read it before any commercial use. Access on Hugging Face requires accepting the publisher’s terms first.
Hugging Face counted about 869,324 downloads of it in the 30 days before we checked (7 Oct 2026), and 1,090 people have liked it there.
It is built on google/medgemma-4b-pt, according to its card.
02
How much memory it needs
We size it the way you would size any model: the weights (parameters × bits ÷ 8), the KV cache for 8,192 tokens of context, and roughly 1.5 GB for the runtime itself. Real use varies with settings.
Each token of context adds about 0.02 MB of KV cache at 16-bit, so 32,768 tokens would add about 0.67 GB. 29 of its 34 layers use sliding-window attention, so the real cache is smaller than this full-attention estimate.
Precision
Weights
KV cache (8,192 tokens)
Total, estimated
BF16 (full precision)
8.6 GB
0.17 GB
10 GB
8-bit (Q8_0)
4.6 GB
0.17 GB
6.2 GB
4-bit (Q4_K)
2.4 GB
0.17 GB
4.1 GB
03
Architecture at a glance
Setting
Value in config.json
Architecture class
Gemma3ForConditionalGeneration
Model type
gemma3
Layers
34
Hidden size
2,560
Attention heads
8
Key-value heads
4 (grouped-query attention)
Head size
256
Vocabulary
262,208
Configured context
131,072
Stored precision
bfloat16
04
Will it fit on ANVE?
How the verdicts are worked out: on ANVE One, about three quarters of 64 GB of unified memory is available to the model; on ANVE Office and ANVE Office Pro, the model has to fit in the 24 GB on the graphics card, with 64 GB or 128 GB of system memory for everything else. This is arithmetic against the launch specification, not a test. ANVE’s tested default is Qwen 3.8 27B; this model is not yet validated on ANVE.
Configuration (at launch)
Memory for the model
Verdict
ANVE One
about 48 GB of 64 GB unified memory
fits at BF16
ANVE Office
24 GB on the RTX PRO 4000 Blackwell SFF Edition
fits at BF16
ANVE Office Pro (GPU)
24 GB on the RTX PRO 4000 Blackwell
fits at BF16
05
What an office would use it for
This size suits quick drafting, classification, extraction and chat over short documents on modest hardware. ANVE pairs a fast 4B-class model with its main model for quick jobs.
06
Variants with the same size
medgemma-1.5-4b-it: the instruction-tuned version for chat (same parameter count, so the memory figures above apply).
07
Similar models to compare
Qwen3-VL-4B-Instruct (4.4B parameters, about 4.0 GB at 4-bit before context; commercial use allowed)
Phi-3.5-vision-instruct (4.1B parameters, about 3.8 GB at 4-bit before context; commercial use allowed)
MiniCPM-V-4 (4.1B parameters, about 3.8 GB at 4-bit before context; commercial use allowed)
08
Questions people ask
How much memory does medgemma-4b-it need?
About 4.1 GB at 4-bit, 6.2 GB at 8-bit and 10 GB at full 16-bit precision, including an 8,192-token KV cache and about 1.5 GB of runtime overhead. These are estimates from its parameter count.
Can medgemma-4b-it run on a 24 GB graphics card?
Yes, at BF16 by this estimate, with room left for context.
Is medgemma-4b-it free for commercial use?
Its card lists a custom licence (health-ai-developer-foundations), which sets its own terms; read it before any commercial use. Read the licence itself before relying on this summary; it is not legal advice.
What is medgemma-4b-it’s context window?
Its configuration sets 131,072 tokens. Longer contexts need more memory for the KV cache, so the practical limit on a given machine can be lower.
09
What is not real yet
No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.
Reservations are ₹999 in India or US$12 elsewhere, refundable in full until you confirm an order in writing (terms). Offices that cannot use cloud AI can apply to be one of ten design partners.
