WeNet (PTB)
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
- Amazon
- Organisation type
- Industry
- Country
- United States of America
- Published
- 8 April 2019
- Authors
- Zhiheng Huang, Bing Xiang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Neural Architecture Search - NAS, Language modeling
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
- 23M
- Training data
- tokens
- Epochs
- 6,000
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
- 3.5 × 10¹⁸ FLOP
- How it was established
- Hardware,Operation counting
GPU hours: 120*60*60*125000000000000*0.3=1.62e+19 Operations: 6*23000000*929000*6000=7.69212e+17 Geometric mean: 3530047365121324032 (3.5e18)
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
- 1
- Wall-clock time
- 120 hours
- Power draw
- 339 W
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
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Benchmark data
- WeNet (PTB)
Sources
Where this record came from and when it was last checked.
- Reference
- WeNet: Weighted Networks for Recurrent Network Architecture Search
- Last updated
- 11 February 2026
What the numbers mean
About this model
WeNet (PTB) was published by Amazon, in United States of America, in April 2019. It comes out of industry.
It works in Language, and is recorded as doing neural Architecture Search - NAS, Language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The training run consumed about 3.5 × 10¹⁸ FLOP, on NVIDIA V100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
WeNet (PTB) — common questions
Is WeNet (PTB) open source?
No. WeNet (PTB) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does WeNet (PTB) have?
WeNet (PTB) has 23M parameters. 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 WeNet (PTB)?
WeNet (PTB) was published by Amazon, based in United States of America, categorised as industry.
When was WeNet (PTB) released?
WeNet (PTB) was published in April 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 WeNet (PTB) used for?
WeNet (PTB) works in Language, and is recorded as handling neural Architecture Search - NAS, Language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train WeNet (PTB)?
Around 3.5 × 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 WeNet (PTB)?
None. WeNet (PTB) 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.
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.