Nemotron-3-Nano-4B The memory it needs, what you may do with it, and the hardware it suits.
Short answer. Nemotron-3-Nano-4B needs about 5.1 GB of memory at 4-bit, 7.1 GB at 8-bit and 11 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 4 billion parameters and a 262,144-token context window. Licence in short: custom licence: check before commercial use. 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
Nemotron-3-Nano-4B is a language model for chat, drafting and question answering, published by NVIDIA on Hugging Face on 7 March 2026. It has about 4 billion parameters and a configured context window of 262,144 tokens.
Its card lists the licence as a custom licence (nvidia-nemotron-open-model-license). In short, the licence sets its own terms; read it before any commercial use.
Hugging Face counted about 2,274,852 downloads of it in the 30 days before we checked (7 Oct 2026), and 128 people have liked it there.
It is built on nvidia/NVIDIA-Nemotron-Nano-9B-v2, 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 0.17 MB of KV cache at 16-bit, so 32,768 tokens would add about 5.6 GB.
Precision
Weights
KV cache (8,192 tokens)
Total, estimated
BF16 (full precision)
7.9 GB
1.4 GB
11 GB
8-bit (Q8_0)
4.2 GB
1.4 GB
7.1 GB
4-bit (Q4_K)
2.2 GB
1.4 GB
5.1 GB
03
Architecture at a glance
Setting
Value in config.json
Architecture class
NemotronHForCausalLM
Model type
nemotron_h
Layers
42
Hidden size
3,136
Attention heads
40
Key-value heads
8 (grouped-query attention)
Head size
128
Vocabulary
131,072
Configured context
262,144
Stored precision
bfloat16
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 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 BF16
ANVE Office Pro (GPU)
24 GB on the RTX PRO 4000 Blackwell
fits at BF16
05
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.
06
Similar models to compare
Qwen3-4B (4.0B parameters, about 3.8 GB at 4-bit before context; commercial use allowed)
Phi-4-mini-instruct (3.8B parameters, about 3.7 GB at 4-bit before context; commercial use allowed)
Phi-4-mini-reasoning (3.8B parameters, about 3.7 GB at 4-bit before context; commercial use allowed)
07
Questions people ask
How much memory does Nemotron-3-Nano-4B need?
About 5.1 GB at 4-bit, 7.1 GB at 8-bit and 11 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 Nemotron-3-Nano-4B run on a 24 GB graphics card?
Yes, at BF16 by this estimate, with room left for context.
Is Nemotron-3-Nano-4B free for commercial use?
Its card lists a custom licence (nvidia-nemotron-open-model-license), which sets its own terms; read it before any commercial use. Read the licence itself before relying on this summary; it is not legal advice.
What is Nemotron-3-Nano-4B’s context window?
Its configuration sets 262,144 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.
