ANVE: the private AI office, on the record. See what that means

ANVE

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

sarvam-30b
The memory it needs, what you may do with it, and the hardware it suits.

Short answer. sarvam-30b needs about 20 GB of memory at 4-bit, 36 GB at 8-bit and 66 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 32.2 billion parameters and a 131,072-token context window. Licence in short: commercial use allowed. By memory alone, on ANVE’s launch configurations: ANVE One fits at 8-bit, ANVE Office fits at 4-bit, ANVE Office Pro (GPU) fits at 4-bit. These are estimates from its published size, not benchmarks.

01

What it is

sarvam-30b is a language model for chat, drafting and question answering, published by Sarvam AI on Hugging Face on 3 March 2026. It has about 32.2 billion parameters and a configured context window of 131,072 tokens. It is a mixture-of-experts model: 128 experts, 6 used for each token.

Its card lists the licence as Apache 2.0. In short, the licence allows commercial use, modification and redistribution, with attribution and a copy of the licence, and includes a patent grant.

Hugging Face counted about 142,704 downloads of it in the 30 days before we checked (7 Oct 2026), and 232 people have liked it there.

The card lists 23 languages: en, hi, bn, ta, te, mr, gu, kn, ml, pa, or, as and 11 more (ISO codes as given on the 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 0.02 MB of KV cache at 16-bit, so 32,768 tokens would add about 0.64 GB.

PrecisionWeightsKV cache (8,192 tokens)Total, estimated
BF16 (full precision)64 GB0.16 GB66 GB
8-bit (Q8_0)34 GB0.16 GB36 GB
4-bit (Q4_K)18 GB0.16 GB20 GB
03

Architecture at a glance

SettingValue in config.json
Architecture classSarvamMoEForCausalLM
Model typesarvam_moe
Layers19
Hidden size4,096
Attention heads64
Key-value heads4 (grouped-query attention)
Head size64
Vocabulary262,144
Experts128, 6 active per token
Configured context131,072
Stored precisionfloat32
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 8-bit
ANVE Office24 GB on the RTX PRO 4000 Blackwell SFF Editionfits at 4-bit
ANVE Office Pro (GPU)24 GB on the RTX PRO 4000 Blackwellfits at 4-bit
05

What an office would use it for

This is the class ANVE runs by default (27B-class): strong enough for meeting minutes, summaries, drafting and question answering over an office’s own documents, and small enough for one box.

06

Similar models to compare

  • granite-4.0-h-small (32.2B parameters, about 20 GB at 4-bit before context; commercial use allowed)
  • Olmo-3-1125-32B (32.2B parameters, about 20 GB at 4-bit before context; commercial use allowed)
  • OLMo-2-0325-32B-Instruct (32.2B parameters, about 20 GB at 4-bit before context; commercial use allowed)
07

Questions people ask

How much memory does sarvam-30b need?
About 20 GB at 4-bit, 36 GB at 8-bit and 66 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 sarvam-30b run on a 24 GB graphics card?
Yes, at 4-bit by this estimate, with room left for context.
Is sarvam-30b free for commercial use?
Its card lists Apache 2.0, which allows commercial use, modification and redistribution, with attribution and a copy of the licence, and includes a patent grant. Read the licence itself before relying on this summary; it is not legal advice.
What is sarvam-30b’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.