TaLK Convolution
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
- Carleton University
- Organisation type
- Academia
- Country
- Canada
- Published
- 8 February 2020
- Authors
- Vasileios Lioutas, Yuhong Guo
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Translation, Text summarization
- 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
- 240M
- Training data
- 103,000,000 tokens
- Epochs
- 182
- Batch size
- 65,536
Table 5 "For the language model, we followed the same configuration as Baevski & Auli (2019). We used 17 decoding layers, each layer with a 1024 hidden size, a 4096 feed-forward hidden size and 8 heads. The adaptive input factor was set to 4."
" We replicated their setup and partition the training data into blocks of 512 contiguous tokens" " same setup as in Baevski & Auli (2019)" https://arxiv.org/abs/1809.10853 in that paper they trained for 286k steps in batches of 65,536 tokens. 286000*65536 / 103000000= 182 epochs (same as in as in Baevski & Auli (2019))
same setup as in Baevski & Auli (2019)
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
- 2.7 × 10¹⁹ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 240000000 parameters * 286000 steps * 65536 tokens per batch = 2.6990346e+19 FLOP
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 GeForce RTX 2080 Ti 11GB
- Chips used
- 8
- Power draw
- 4.1 kW
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
- Unreleased
MIT, code and weights (though this repo is for translation not WT-103): https://github.com/lioutasb/TaLKConvolutions?tab=readme-ov-file
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
- 30
- Benchmark data
- TaLK Convolution
"[We] set a new state-of-the-art result on the IWSLT De-En and CNN-DailyMail datasets"
Sources
Where this record came from and when it was last checked.
- Reference
- Time-aware Large Kernel Convolutions
- Last updated
- 28 November 2025
What the numbers mean
About this model
TaLK Convolution was published by Carleton University, in Canada, in February 2020. The organisation is categorised as academia.
It works in Language, and is recorded as doing language modeling, Translation, Text summarization.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
The training run consumed about 2.7 × 10¹⁹ FLOP, on NVIDIA GeForce RTX 2080 Ti 11GB. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Around 103,000,000 tokens went into training it.
Its inclusion criterion is sOTA improvement.
Answers
TaLK Convolution — common questions
How much compute was used to train TaLK Convolution?
Around 2.7 × 10¹⁹ FLOP, on NVIDIA GeForce RTX 2080 Ti 11GB. 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 TaLK Convolution?
None. TaLK Convolution 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 TaLK Convolution open source?
No. TaLK Convolution has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does TaLK Convolution have?
TaLK Convolution has 240M parameters. Table 5 "For the language model, we followed the same configuration as Baevski & Auli (2019). We used 17 decoding layers, each layer with a 1024 hidden size, a 4096 feed-forward hidden size and 8 heads. The adaptive input factor was set to 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 TaLK Convolution?
TaLK Convolution was published by Carleton University, based in Canada, categorised as academia.
When was TaLK Convolution released?
TaLK Convolution was published in February 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 TaLK Convolution used for?
TaLK Convolution works in Language, and is recorded as handling language modeling, Translation, Text summarization. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.