DeLighT
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
- University of Washington,Allen Institute for AI,Facebook AI Research
- Organisation type
- Academia,Research collective,Industry
- Country
- United States of America, France
- Published
- 3 August 2020
- Authors
- Sachin Mehta, Marjan Ghazvininejad, Srinivasan Iyer, Luke Zettlemoyer, Hannaneh Hajishirzi
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Translation
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
- 99M
- Training data
- 103,000,000 tokens
- Epochs
- 62.14
- Batch size
- 64,000
99M (Table 4b)
"100K iterations with a context length of 512 and an effective batch size of 64K tokens." 100000*64000/103000000 = 62.14 epochs
"effective batch size of 64K 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
- 3.8 × 10¹⁸ FLOP
- How it was established
- Operation counting,Hardware
6 FLOP / parameter / token * 99 * 10^6 parameters * 100000 steps * 64000 tokens per batch = 3.8016e+18 FLOP 31330000000000 FLOP / second / GPU * 8 GPUs * 30 hours [assumed based on smaller models reported training time] * 3600 sec / hour * 0.3 [assumed utilization] = 8.120736e+18 FLOP Operation counting method uses less assumptions
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 V100
- Chips used
- 8
- Wall-clock time
- 30 hours
- Power draw
- 4.9 kW
Table 5 reports training time for 54M translation model (23h) it should be more for the 99M language modeling model.
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
MIT, training and evaluation for WT103: https://github.com/sacmehta/delight/blob/master/readme_files/lm/wikitext103.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
- SOTA improvement
- Record confidence
- Likely
- Citations
- 98
- Benchmark data
- DeLight
"Comparison with state-of-the-art methods on machine translation corpora. DeLighT delivers similar or better performance than state-of-the-art models with fewer parameters."
Sources
Where this record came from and when it was last checked.
- Reference
- DeLighT: Deep and Light-weight Transformer
- Last updated
- 28 November 2025
What the numbers mean
Background
DeLighT was published by University of Washington,Allen Institute for AI,Facebook AI Research, in United States of America, in August 2020. academia,Research collective,Industry is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling, Translation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Producing it required around 3.8 × 10¹⁸ FLOP of arithmetic, on NVIDIA V100, which is a statement about the training budget rather than about inference.
The training set ran to roughly 103,000,000 tokens.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
DeLighT — common questions
How much compute was used to train DeLighT?
Around 3.8 × 10¹⁸ FLOP, on NVIDIA V100. 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 DeLighT?
None. DeLighT 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 DeLighT open source?
No. DeLighT has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does DeLighT have?
DeLighT has 99M parameters. 99M (Table 4b). 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 DeLighT?
DeLighT was published by University of Washington,Allen Institute for AI,Facebook AI Research, based in United States of America, categorised as academia,Research collective,Industry.
When was DeLighT released?
DeLighT was published in August 2020. 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 DeLighT used for?
DeLighT works in Language, and is recorded as handling language modeling, Translation. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.