Llama-3_3-Nemotron-Super-49B-v1_5 How much memory it needs, what its licence allows, and whether it fits an office box.
Short answer. Llama-3_3-Nemotron-Super-49B-v1_5 needs about 51 GB of memory at 4-bit, 76 GB at 8-bit and 123 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 49.9 billion parameters and a 131,072-token context window. Licence in short: custom licence: check before commercial use. 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-3_3-Nemotron-Super-49B-v1_5 is a language model for chat, drafting and question answering, published by NVIDIA on Hugging Face on 25 July 2025. It has about 49.9 billion parameters and a configured context window of 131,072 tokens.
Its card lists the licence as a custom licence (nvidia-open-model-license). In short, the licence sets its own terms; read it before any commercial use.
Hugging Face counted about 63,085 downloads of it in the 30 days before we checked (7 Oct 2026), and 236 people have liked it there.
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 2.62 MB of KV cache at 16-bit, so 32,768 tokens would add about 86 GB.
Precision
Weights
KV cache (8,192 tokens)
Total, estimated
BF16 (full precision)
100 GB
21 GB
123 GB
8-bit (Q8_0)
53 GB
21 GB
76 GB
4-bit (Q4_K)
28 GB
21 GB
51 GB
03
Architecture at a glance
Setting
Value in config.json
Architecture class
DeciLMForCausalLM
Model type
nemotron-nas
Layers
80
Hidden size
8,192
Attention heads
64
Head size
128
Vocabulary
128,256
Configured context
131,072
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
does not fit
ANVE Office
24 GB on the RTX PRO 4000 Blackwell SFF Edition
only with partial offload to system memory, which is much slower
ANVE Office Pro (GPU)
24 GB on the RTX PRO 4000 Blackwell
only 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
Variants with the same size
Llama-3_3-Nemotron-Super-49B-v1: a variant with the same size and memory needs (same parameter count, so the memory figures above apply).
07
Similar models to compare
Kimi-Linear-48B-A3B-Instruct (49.1B parameters, about 29 GB at 4-bit before context; commercial use allowed)
Phi-3.5-MoE-instruct (41.9B parameters, about 25 GB at 4-bit before context; commercial use allowed)
Seed-OSS-36B-Instruct (36.2B parameters, about 22 GB at 4-bit before context; commercial use allowed)
08
Questions people ask
How much memory does Llama-3_3-Nemotron-Super-49B-v1_5 need?
About 51 GB at 4-bit, 76 GB at 8-bit and 123 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-Nemotron-Super-49B-v1_5 run on a 24 GB graphics card?
Not entirely: even at 4-bit it needs about 51 GB, so part of it would sit in system memory, which is much slower.
Is Llama-3_3-Nemotron-Super-49B-v1_5 free for commercial use?
Its card lists a custom licence (nvidia-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 Llama-3_3-Nemotron-Super-49B-v1_5’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.
09
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.
