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

Llama-3.3-70B-Instruct
The memory it needs, what you may do with it, and the hardware it suits.

Short answer. Llama-3.3-70B-Instruct needs about 44 GB of memory at 4-bit, 79 GB at 8-bit and 145 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 70.6 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 4-bit, 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-3.3-70B-Instruct is a language model for chat, drafting and question answering, published by Meta on Hugging Face on 26 November 2024. It has about 70.6 billion parameters and a configured context window of 131,072 tokens.

Its card lists the licence as Llama 3.3 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 366,023 downloads of it in the 30 days before we checked (7 Oct 2026), and 3,109 people have liked it there.

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

It is built on meta-llama/Llama-3.1-70B, according to its card.

02

How much memory it needs

We size it the way you would size any model: the weights (parameters × bits ÷ 8), the KV cache for 8,192 tokens of context, and roughly 1.5 GB for the runtime itself. Real use varies with settings.

Each token of context adds about 0.33 MB of KV cache at 16-bit, so 32,768 tokens would add about 11 GB.

PrecisionWeightsKV cache (8,192 tokens)Total, estimated
BF16 (full precision)141 GB2.7 GB145 GB
8-bit (Q8_0)75 GB2.7 GB79 GB
4-bit (Q4_K)40 GB2.7 GB44 GB
03

Architecture at a glance

SettingValue in config.json
Architecture classLlamaForCausalLM
Model typellama
Layers80
Hidden size8,192
Attention heads64
Key-value heads8 (grouped-query attention)
Head size128
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 4-bit
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

  • DeepSeek-R1-Distill-Llama-70B (70.6B parameters, about 41 GB at 4-bit before context; commercial use allowed)
  • Apertus-70B-2509 (70.6B parameters, about 41 GB at 4-bit before context; commercial use allowed)
  • Qwen3-Coder-Next (79.7B parameters, about 46 GB at 4-bit before context; commercial use allowed)
07

Questions people ask

How much memory does Llama-3.3-70B-Instruct need?
About 44 GB at 4-bit, 79 GB at 8-bit and 145 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-3.3-70B-Instruct run on a 24 GB graphics card?
Not entirely: even at 4-bit it needs about 44 GB, so part of it would sit in system memory, which is much slower.
Is Llama-3.3-70B-Instruct free for commercial use?
Its card lists Llama 3.3 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-3.3-70B-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.