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

Llama-4-Scout-17B-16E-Instruct
Memory at each precision, the licence in plain words, and where it fits.

Short answer. Llama-4-Scout-17B-16E-Instruct needs about 64 GB of memory at 4-bit, 119 GB at 8-bit and 220 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 108.6 billion parameters. Licence in short: commercial use allowed, with conditions. By memory alone, on ANVE’s launch configurations: ANVE One does not fit, ANVE Office only with partial offload to system memory, which is much slower, ANVE Office Pro (GPU) only with partial offload to system memory, which is much slower. These are estimates from its published size, not benchmarks.

01

What it is

Llama-4-Scout-17B-16E-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 2 April 2025. It has about 108.6 billion parameters. It is a mixture-of-experts model: 16 experts, 1 used for each token.

Its card lists the licence as Llama 4 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 171,408 downloads of it in the 30 days before we checked (7 Oct 2026), and 1,367 people have liked it there.

The card lists 12 languages: ar, de, en, es, fr, hi, id, it, pt, th, tl, vi (ISO codes as given on the card).

It is built on meta-llama/Llama-4-Scout-17B-16E, according to its card.

02

How much memory it needs

How these figures are worked out: weights = parameters × bits per weight ÷ 8. The KV cache is added for 8,192 tokens of context, plus about 1.5 GB of runtime overhead. Treat the totals as planning estimates.

Each token of context adds about 0.20 MB of KV cache at 16-bit, so 32,768 tokens would add about 6.4 GB.

PrecisionWeightsKV cache (8,192 tokens)Total, estimated
BF16 (full precision)217 GB1.6 GB220 GB
8-bit (Q8_0)115 GB1.6 GB119 GB
4-bit (Q4_K)61 GB1.6 GB64 GB
03

Architecture at a glance

SettingValue in config.json
Architecture classLlama4ForConditionalGeneration
Model typellama4
Layers48
Hidden size5,120
Attention heads40
Key-value heads8 (grouped-query attention)
Head size128
Vocabulary202,048
Experts16, 1 active per token
Configured context10,485,760
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 memorydoes not fit
ANVE Office24 GB on the RTX PRO 4000 Blackwell SFF Editiononly with partial offload to system memory, which is much slower
ANVE Office Pro (GPU)24 GB on the RTX PRO 4000 Blackwellonly with partial offload to system memory, which is much slower
05

What an office would use it for

Models this large are stronger on long, hard drafting and reasoning, but need most of a 64 to 128 GB machine even at 4-bit, which leaves less room for long documents and several users.

06

Similar models to compare

  • GLM-4.5V (107.7B parameters, about 62 GB at 4-bit before context; commercial use allowed)
  • Qwen3.5-122B-A10B (125.1B parameters, about 72 GB at 4-bit before context; commercial use allowed)
  • Qwen3.8-Flash-Next (180.0B parameters, about 103 GB at 4-bit before context; custom licence: check before commercial use)
07

Questions people ask

How much memory does Llama-4-Scout-17B-16E-Instruct need?
About 64 GB at 4-bit, 119 GB at 8-bit and 220 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-4-Scout-17B-16E-Instruct run on a 24 GB graphics card?
Not entirely: even at 4-bit it needs about 64 GB, so part of it would sit in system memory, which is much slower.
Is Llama-4-Scout-17B-16E-Instruct free for commercial use?
Its card lists Llama 4 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.
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.