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

gemma-3n-E2B-it
The memory it needs, what you may do with it, and the hardware it suits.

Short answer. gemma-3n-E2B-it needs about 4.7 GB of memory at 4-bit, 7.4 GB at 8-bit and 12 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 5.4 billion parameters and a 32,768-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.

01

What it is

gemma-3n-E2B-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 12 June 2025. It has about 5.4 billion parameters and a configured context window of 32,768 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 173,212 downloads of it in the 30 days before we checked (7 Oct 2026), and 331 people have liked it there.

It is built on google/gemma-3n-E4B-it, according to its card.

02

How much memory it needs

The arithmetic: parameters × bits per weight ÷ 8 gives the weights; add the KV cache, which grows with every token of context; then add about 1.5 GB for the runtime. These are estimates for planning, not measurements.

Each token of context adds about 0.01 MB of KV cache at 16-bit, so 32,768 tokens would add about 0.40 GB. 24 of its 30 layers use sliding-window attention, so the real cache is smaller than this full-attention estimate.

PrecisionWeightsKV cache (8,192 tokens)Total, estimated
BF16 (full precision)11 GB0.10 GB12 GB
8-bit (Q8_0)5.8 GB0.10 GB7.4 GB
4-bit (Q4_K)3.1 GB0.10 GB4.7 GB
03

Architecture at a glance

SettingValue in config.json
Architecture classGemma3nForConditionalGeneration
Model typegemma3n
Layers30
Hidden size2,048
Attention heads8
Key-value heads2 (grouped-query attention)
Head size256
Vocabulary262,400
Configured context32,768
Stored precisionbfloat16
04

Will it fit on ANVE?

These verdicts are arithmetic, not tests, against the specification at launch: ANVE One can give a model about three quarters of its 64 GB of unified memory, while ANVE Office and ANVE Office Pro hold it on a 24 GB graphics card and keep the rest of the stack in 64 GB or 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 modelVerdict
ANVE Oneabout 48 GB of 64 GB unified memoryfits at BF16
ANVE Office24 GB on the RTX PRO 4000 Blackwell SFF Editionfits at BF16
ANVE Office Pro (GPU)24 GB on the RTX PRO 4000 Blackwellfits 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

Similar models to compare

  • Molmo2-4B (4.9B parameters, about 4.2 GB at 4-bit before context; commercial use allowed)
  • 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)
07

Questions people ask

How much memory does gemma-3n-E2B-it need?
About 4.7 GB at 4-bit, 7.4 GB at 8-bit and 12 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-3n-E2B-it run on a 24 GB graphics card?
Yes, at BF16 by this estimate, with room left for context.
Is gemma-3n-E2B-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-3n-E2B-it’s context window?
Its configuration sets 32,768 tokens. Longer contexts need more memory for the KV cache, so the practical limit on a given machine can be lower.
08

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.