Big Transformer for Back-Translation
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- Facebook AI Research,Google Brain
- Organisation type
- Industry,Industry
- Country
- United States of America, France
- Published
- 28 August 2018
- Authors
- Sergey Edunov, Myle Ott, Michael Auli, David Grangier
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Translation
- Approach
- Supervised
- 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.
- Training data
- 4,520,000,000 tokens
"Finally, for WMT English-German we train on all 226M available monolingual training sentences and perform 250K updates in 22.5 hours on 128 GPUs." We assume that 1 sentence have 15 words
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
- 4.8 × 10²⁰ FLOP
- How it was established
- Hardware
(128) * (1.25e14) * (27*3600 + 40*60) * (0.3) = 4.7808e20 (number of gpus) * (peak flops) * (seconds) * (assumed utilization rate) "We run experiments on DGX-1 machines with 8Nvidia V100 GPUs and machines are interconnected by Infiniband. Experiments are run on 16 machines and we perform 30K synchronous updates." "We also use the NCCL2 library [...] with 16-bit floating point operations" NCCL2 supported tensor core operations at 1.25e14 FLOP/s on a V100 for FP16 in section 5.6 we have "t…
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 V100 DGXS 16 GB
- Chips used
- 128
- Chip-hours
- 3,541
- Wall-clock time
- 28 hours
- Power draw
- 66.2 kW
- Compute cost
- $2,442
"training updates in 27h 40min on 128 GPUs"
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Open source
Code and weights, MIT license: https://github.com/facebookresearch/fairseq/blob/main/examples/backtranslation/README.md
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited,SOTA improvement
- Record confidence
- Likely
- Citations
- 1,155
"Finally, we scale to hundreds of millions of monolingual sentences and achieve a new state of the art of 35 BLEU on the WMT'14 English-German test set. "
Sources
Where this record came from and when it was last checked.
- Reference
- Understanding Back-Translation at Scale
- Last updated
- 28 November 2025
What the numbers mean
Background
Big Transformer for Back-Translation was published by Facebook AI Research,Google Brain, in the country recorded as United States of America, during August 2018. The publishing organisation is categorised as industry,Industry.
It works in the domain of Language, and is recorded as performing the task of translation.
Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.
What went into building it
Training it took a computation budget of roughly 4.8 × 10²⁰ FLOP, on hardware recorded as NVIDIA Tesla V100 DGXS 16 GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 4,520,000,000 tokens of text.
The reason it appears in this catalogue at all: highly cited,SOTA improvement.
Answers
Big Transformer for Back-Translation — common questions
Big Transformer for Back-Translation— when was it released?
It was published in August 2018. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Big Transformer for Back-Translation— what is it used for?
It works in the domain of Language, and is recorded as handling the task of translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Big Transformer for Back-Translation— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
Big Transformer for Back-Translation— how much compute was used to train it?
Training consumed around 4.8 × 10²⁰ FLOP, on hardware recorded as NVIDIA Tesla V100 DGXS 16 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.
Big Transformer for Back-Translation— what GPU do I need to run it?
We cannot say. It has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Big Transformer for Back-Translation— is it open source?
Its weights are published, so it can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
Big Transformer for Back-Translation— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Big Transformer for Back-Translation— who created it?
It was published by Facebook AI Research,Google Brain, based in United States of America, an organisation categorised as industry,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.