Megatron-BERT
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- NVIDIA
- Organisation type
- Industry
- Country
- United States of America
- Published
- 17 September 2019
- Authors
- Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, Bryan Catanzaro
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation
- Approach
- Self-supervised learning
- Numerical format
- FP16
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 3.9B
- Training data
- 6,960,000,000 tokens
- Batch size
- 524,288
Table 4
"The resulting aggregate corpus contains 174 GB of deduplicated text." 174e9 bytes * (1 word / 5 bytes) * (4 tokens / 3 words) = 4.64e10 tokens
"we set the batch size to 1024 and use a learning rate of 1.0e4 warmed up over 10,000 iterations and decayed linearly over 2 million iterations. Other training parameters are kept the same as (Devlin et al., 2018)." in Devlin et al (BERT), sequences are 512 tokens
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 2.2 × 10²² FLOP
- How it was established
- Operation counting,Third-party estimation
A third-party source: https://lair.lighton.ai/akronomicon/ claims 5.7e22 The authors report experimenting on 1 V100 GPU and achieving throughput of 39 TFLOPS which is 30% of the peak throughput. Therefore the GPU has a peak throughput of 130 TFLOPS so it is specifically the NVIDIA V100S PCIe. https://images.nvidia.com/content/technologies/volta/pdf/volta-v100-datasheet-update-us-1165301-r5.pdf Param-based calculation: 6ND = 6*3.9e9*(2e6+1e4)*1024*512 = 2.5e22 FLOP 1024 is the batch size, 512 …
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA Tesla V100S PCIe 32 GB
- Chips used
- 512
- Chip-hours
- 191,488
- Wall-clock time
- 374 hours (15.6 days)
- Power draw
- 262.6 kW
- Compute cost
- $171,819
The 8.3B GPT-like arch took 2.1 days per epoch on 512 GPUs, batch size 512, sequence length 1024. An epoch was 68.5k iterations. BERT: batch size 1024, sequence length 512, 2e6 iterations total. Halving the model size should ~halve the iteration time. Doubling the batch size should ~double the iteration time. Halving the sequence length should ~quarter the iteration time (quadratic scaling). Hence 3.1e-5 days/iteration * 2 * 1/2 * 1/4 = 7.8e-6 days/iteration. 2e6 iterations => seems like 15.…
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open source
training code https://github.com/NVIDIA/Megatron-LM/blob/main/pretrain_bert.py MIT-like license: https://github.com/NVIDIA/Megatron-LM/blob/main/LICENSE
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Why it is tracked
- Highly cited,SOTA improvement
- Record confidence
- Confident
- Citations
- 2,766
"Our BERT model achieves SOTA results on the RACE dataset"
Sources
Where this record came from and when it was last checked.
- Reference
- Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Megatron-BERT was published by NVIDIA, in United States of America, in September 2019. It comes out of industry.
It works in Language, and is recorded as doing language modeling/generation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
The training run consumed about 2.2 × 10²² FLOP, on NVIDIA Tesla V100S PCIe 32 GB. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
It was trained on about 6,960,000,000 tokens of text.
Its inclusion criterion is highly cited,SOTA improvement.
Answers
Megatron-BERT — common questions
When was Megatron-BERT released?
Megatron-BERT was published in September 2019. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Megatron-BERT used for?
Megatron-BERT works in Language, and is recorded as handling language modeling/generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train Megatron-BERT?
Around 2.2 × 10²² FLOP, on NVIDIA Tesla V100S PCIe 32 GB. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run Megatron-BERT?
None. Megatron-BERT is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Megatron-BERT open source?
No. Megatron-BERT has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Megatron-BERT have?
Megatron-BERT has 3.9B parameters. Table 4. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Megatron-BERT?
Megatron-BERT was published by NVIDIA, based in United States of America, categorised as industry.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.