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

gemma-2-2b-it
How much memory it needs, what its licence allows, and whether it fits an office box.

Short answer. gemma-2-2b-it needs about 3.8 GB of memory at 4-bit, 5.2 GB at 8-bit and 7.6 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 2.6 billion parameters and an 8,192-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-2-2b-it is a language model for chat, drafting and question answering, published by Google on Hugging Face on 16 July 2024. It has about 2.6 billion parameters and a configured context window of 8,192 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 547,931 downloads of it in the 30 days before we checked (7 Oct 2026), and 1,554 people have liked it there.

It is built on google/gemma-2-2b, 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 0.11 MB of KV cache at 16-bit, so 32,768 tokens would add about 3.5 GB.

PrecisionWeightsKV cache (8,192 tokens)Total, estimated
BF16 (full precision)5.2 GB0.87 GB7.6 GB
8-bit (Q8_0)2.8 GB0.87 GB5.2 GB
4-bit (Q4_K)1.5 GB0.87 GB3.8 GB
03

Architecture at a glance

SettingValue in config.json
Architecture classGemma2ForCausalLM
Model typegemma2
Layers26
Hidden size2,304
Attention heads8
Key-value heads4 (grouped-query attention)
Head size256
Vocabulary256,000
Configured context8,192
Stored precisionbfloat16
04

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 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

  • LFM2.5-2.6B (2.7B parameters, about 3.0 GB at 4-bit before context; custom licence: check before commercial use)
  • granite-3.3-2b-instruct (2.5B parameters, about 2.9 GB at 4-bit before context; commercial use allowed)
  • MiniCPM5-2B (2.5B parameters, about 2.9 GB at 4-bit before context; commercial use allowed)
07

Questions people ask

How much memory does gemma-2-2b-it need?
About 3.8 GB at 4-bit, 5.2 GB at 8-bit and 7.6 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-2-2b-it run on a 24 GB graphics card?
Yes, at BF16 by this estimate, with room left for context.
Is gemma-2-2b-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-2-2b-it’s context window?
Its configuration sets 8,192 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.