Project AURIONAI Use-Cases Research & Innovation
NVIDIA
LLM

Nemotron-4 340B

Nemotron-4 340B is an NVIDIA open model family (Base, Instruct, Reward) built to generate synthetic training data for other LLMs.

Nemotron-4 340B synthetic data generation pipeline diagram from NVIDIA's announcement.
Levelmodel
ClassLLM
Parameters340B
Context4,096
Released14 Jun 2024
Opennessopen weights permissive
Runs onmulti-GPU node
Statusnot in the notes

Benchmark Results

vendor-reported
GSM8Kmath
Instruct model; 0-shotvendor-reported
92.3
050100

Score in %. Higher is better.

HumanEvalcoding
Instruct model; 0-shotvendor-reported
73.2
050100

Score in %. Higher is better.

IFEvalknowledge and reasoning
Instruct model; prompt-strict accuracyvendor-reported
79.9
050100

Score in %. Higher is better.

MMLUknowledge and reasoning
Instruct model; 0-shotvendor-reported
78.7
050100

Score in %. Higher is better.

MT-Benchknowledge and reasoning
Instruct model; judged by GPT-4-0125-Previewvendor-reported
8.22
04.59

Score in score. Higher is better.

Where this comes from
Every score names who ran it: vendor-reported, maintainer-run or independent. A rank is shown with the population it was ranked in (gap rule 3).

Lineage

instruct of
Nemotron-4-340B-Base to Nemotron-4 340B: instruct of, high confidenceNemotron-4-340B-BaseNemotron-4-340B-BaseNVIDIANemotron-4 340BNemotron-4 340BNVIDIA, Jun 2024
NVIDIANemotron-4-340B-Base is the parent (instruct of)high confidencemodel card
Where this comes from
Parent and child relations from model cards, Hugging Face base_model fields and the genealogy tables of the research notes. The tree shows two generations each way, 2 models. Confidence and evidence are the notes' own.

R&D Topics

Identity

Developer
NVIDIA
Family
Nemotron
Released
14 Jun 2024 sourcesannouncement page
Weights published
13 Jun 2024
Record checked
11 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.

Architecture and Size

Architecture
dense transformer Decoder-only transformer with GQA and RoPE; family has Base, Instruct and Reward models
Total parameters
340B sources
Active parameters
–
Checkpoint parameters
–
Context length
4,096 tokens
Input
text
Output
text
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).

Openness and Access

Openness
open weights permissive
Licence
NVIDIA Open Model License sources
Access routes
open weights download, API
Where this comes from
The licence file governs over registry tags and aggregator labels.

Deployment

Runs on
multi-GPU node
Footprint
BF16 inference: 8x H200 (one node), 16x H100 or 16x A100 80GB (two nodes)
Runtimes
not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.

Indonesia

Indonesian support
not stated
Regional languages
not in the notes
Jakarta hosting
unknown
Languages
Instruct model intended for English; base pre-trained on English text, 50+ natural languages and 40+ coding languages
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.

Industry Use Cases

No industry use case links this model.

Sources

  1. blogs.nvidia.com/blog/nemotron-4-synthetic-data-generation-llm... https://blogs.nvidia.com/blog/nemotron-4-synthetic-data-generation-llm-training/
  2. huggingface.co/nvidia/Nemotron-4-340B-Instruct https://huggingface.co/nvidia/Nemotron-4-340B-Instruct
  3. huggingface.co/api/models/nvidia/Nemotron-4-340B-Instruct https://huggingface.co/api/models/nvidia/Nemotron-4-340B-Instruct

To cite this page: Project AURION, AI Use-Cases Research & Innovation, "Nemotron-4 340B", https://aurion.semai.id/models/nvidia-nemotron-4-340b/, records of October 2026.