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

Llama-3.2-1B-Instruct
The memory it needs, what you may do with it, and the hardware it suits.

Short answer. Llama-3.2-1B-Instruct needs about 2.5 GB of memory at 4-bit, 3.1 GB at 8-bit and 4.2 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 1.2 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.

01

What it is

Llama-3.2-1B-Instruct is a language model for chat, drafting and question answering, published by Meta on Hugging Face on 18 September 2024. It has about 1.2 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 7,534,738 downloads of it in the 30 days before we checked (7 Oct 2026), and 1,795 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).

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 0.03 MB of KV cache at 16-bit, so 32,768 tokens would add about 1.1 GB.

PrecisionWeightsKV cache (8,192 tokens)Total, estimated
BF16 (full precision)2.5 GB0.27 GB4.2 GB
8-bit (Q8_0)1.3 GB0.27 GB3.1 GB
4-bit (Q4_K)0.70 GB0.27 GB2.5 GB
03

Architecture at a glance

SettingValue in config.json
Architecture classLlamaForCausalLM
Model typellama
Layers16
Hidden size2,048
Attention heads32
Key-value heads8 (grouped-query attention)
Head size64
Vocabulary128,256
Configured context131,072
Stored precisionbfloat16
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 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

Models this small are best for routing, classification, autocomplete and simple extraction. They are not reliable for minutes or legal drafting: ANVE never uses anything smaller than a 4B-class model for minutes.

06

Similar models to compare

  • LFM2.5-1.2B-Instruct (1.2B parameters, about 2.2 GB at 4-bit before context; custom licence: check before commercial use)
  • LFM2-1.2B (1.2B parameters, about 2.2 GB at 4-bit before context; custom licence: check before commercial use)
  • granite-3.0-1b-a400m-instruct (1.3B parameters, about 2.3 GB at 4-bit before context; commercial use allowed)
07

Questions people ask

How much memory does Llama-3.2-1B-Instruct need?
About 2.5 GB at 4-bit, 3.1 GB at 8-bit and 4.2 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-3.2-1B-Instruct run on a 24 GB graphics card?
Yes, at BF16 by this estimate, with room left for context.
Is Llama-3.2-1B-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-3.2-1B-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.
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.