How much memory does Llama-3.2-11B-Vision-Instruct need?

About 8.8 GB at 4-bit, 14 GB at 8-bit and 24 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 Llama-3.2-11B-Vision-Instruct run on a 24 GB graphics card?

Yes, at 8-bit by this estimate, with room left for context.

What is Llama-3.2-11B-Vision-Instruct’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

Llama-3.2-11B-Vision-Instruct
Memory at each precision, the licence in plain words, and where it fits.

Short answer. Llama-3.2-11B-Vision-Instruct needs about 8.8 GB of memory at 4-bit, 14 GB at 8-bit and 24 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 10.7 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 8-bit, ANVE Office Pro (GPU) fits at 8-bit. These are estimates from its published size, not benchmarks.

What it is

Llama-3.2-11B-Vision-Instruct is a vision-language model: it reads images (scanned pages, photos, charts and screenshots) as well as text, published by Meta on Hugging Face on 18 September 2024. It has about 10.7 billion parameters and a configured context window of 131,072 tokens.

Its card lists the licence as Llama 3.2 Community License. In short, the licence allows commercial use under Meta’s conditions, including its acceptable use policy and a separate licence for companies above 700 million monthly active users. Access on Hugging Face requires accepting the publisher’s terms first.

Hugging Face counted about 67,895 downloads of it in the 30 days before we checked (7 Oct 2026), and 1,671 people have liked it there.

The card lists 8 languages: en, de, fr, it, pt, hi, es, th (ISO codes as given on the card).

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 0.16 MB of KV cache at 16-bit, so 32,768 tokens would add about 5.4 GB.

Precision	Weights	KV cache (8,192 tokens)	Total, estimated
BF16 (full precision)	21 GB	1.3 GB	24 GB
8-bit (Q8_0)	11 GB	1.3 GB	14 GB
4-bit (Q4_K)	6.0 GB	1.3 GB	8.8 GB

Architecture at a glance

Setting	Value in config.json
Architecture class	MllamaForConditionalGeneration
Model type	mllama
Layers	40
Hidden size	4,096
Attention heads	32
Key-value heads	8 (grouped-query attention)
Head size	128
Vocabulary	128,256
Configured context	131,072
Stored precision	bfloat16

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 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 8-bit
ANVE Office Pro (GPU)	24 GB on the RTX PRO 4000 Blackwell	fits at 8-bit

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

GLM-4.1V-9B-Thinking (10.3B parameters, about 7.3 GB at 4-bit before context; commercial use allowed)
GLM-4.6V-Flash (10.3B parameters, about 7.3 GB at 4-bit before context; commercial use allowed)
gemma-3-12b-it (12.2B parameters, about 8.4 GB at 4-bit before context; commercial use allowed, with conditions)

Questions people ask

How much memory does Llama-3.2-11B-Vision-Instruct need?: About 8.8 GB at 4-bit, 14 GB at 8-bit and 24 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 Llama-3.2-11B-Vision-Instruct run on a 24 GB graphics card?: Yes, at 8-bit by this estimate, with room left for context.
Is Llama-3.2-11B-Vision-Instruct free for commercial use?: Its card lists Llama 3.2 Community License, which allows commercial use under Meta’s conditions, including its acceptable use policy and a separate licence for companies above 700 million monthly active users. Read the licence itself before relying on this summary; it is not legal advice.
What is Llama-3.2-11B-Vision-Instruct’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.

Llama-3.2-11B-Vision-InstructMemory at each precision, the licence in plain words, and where it fits.