How much memory does gemma-3-4b-it need?

About 5.1 GB at 4-bit, 7.2 GB at 8-bit and 11 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 gemma-3-4b-it run on a 24 GB graphics card?

Yes, at BF16 by this estimate, with room left for context.

What is gemma-3-4b-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.

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ANVE

Run it on your own hardware · updated 2026-10-16

gemma-3-4b-it
How much memory it needs, what its licence allows, and whether it fits an office box.

Short answer. gemma-3-4b-it needs about 5.1 GB of memory at 4-bit, 7.2 GB at 8-bit and 11 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: commercial use allowed, with conditions. 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.

What it is

gemma-3-4b-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 20 February 2025. It has about 4.3 billion parameters and a configured context window of 131,072 tokens.

Its card lists the licence as Gemma Terms of Use. In short, the licence allow commercial use, subject to Google’s prohibited use policy, and require the terms to be passed on with the model. Access on Hugging Face requires accepting the publisher’s terms first.

Hugging Face counted about 1,240,056 downloads of it in the 30 days before we checked (7 Oct 2026), and 1,561 people have liked it there.

It is built on google/gemma-3-4b-pt, according to its card.

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 0.14 MB of KV cache at 16-bit, so 32,768 tokens would add about 4.6 GB. Some 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	1.1 GB	11 GB
8-bit (Q8_0)	4.6 GB	1.1 GB	7.2 GB
4-bit (Q4_K)	2.4 GB	1.1 GB	5.1 GB

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

Will it fit on ANVE?

Arithmetic against the launch specification, not a test: about three quarters of ANVE One’s 64 GB of unified memory for the model (as AMD’s Variable Graphics Memory allows), and the 24 GB on the graphics card in ANVE Office and ANVE Office Pro, which keep the rest of the stack in 64 GB and 128 GB of system memory. 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

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.

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)

Questions people ask

How much memory does gemma-3-4b-it need?: About 5.1 GB at 4-bit, 7.2 GB at 8-bit and 11 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 gemma-3-4b-it run on a 24 GB graphics card?: Yes, at BF16 by this estimate, with room left for context.
Is gemma-3-4b-it free for commercial use?: Its card lists Gemma Terms of Use, which allow commercial use, subject to Google’s prohibited use policy, and require the terms to be passed on with the model. Read the licence itself before relying on this summary; it is not legal advice.
What is gemma-3-4b-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.

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.

gemma-3-4b-itHow much memory it needs, what its licence allows, and whether it fits an office box.