RGC+ASQ (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
- Tsinghua University,University of California Los Angeles (UCLA)
- Organisation type
- Academia,Academia
- Country
- China, United States of America
- Published
- 13 August 2018
- Authors
- Jiarui Fang, Haohuan Fu, Guangwen Yang, Cho-Jui Hsieh
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- 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
- 53.5M
- Training data
- tokens
- Epochs
- 40
Table 1: 204MB parameters. Assuming they used FP32: 204*1024*1024/4=53477376
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
- 1.2 × 10¹⁶ FLOP
6*53477376*929000*40=1.1923316e+16
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 Titan V
- 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
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 29
- Benchmark data
- RGC+ASQ (PTB)
Sources
Where this record came from and when it was last checked.
- Reference
- RedSync : Reducing Synchronization Traffic for Distributed Deep Learning
- Last updated
- 25 May 2026
What the numbers mean
About this model
RGC+ASQ (PTB) was published by Tsinghua University,University of California Los Angeles (UCLA), in the country recorded as China, during August 2018. The publishing organisation is categorised as academia,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Training it took a computation budget of roughly 1.2 × 10¹⁶ FLOP, on hardware recorded as NVIDIA Titan V. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
RGC+ASQ (PTB) — common questions
RGC+ASQ (PTB)— how many parameters does it have?
It has a parameter count of 53.5M. Table 1: 204MB parameters. Assuming they used FP32: 204*1024*1024/4=53477376. 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.
RGC+ASQ (PTB)— who created it?
It was published by Tsinghua University,University of California Los Angeles (UCLA), based in China, an organisation categorised as academia,Academia.
RGC+ASQ (PTB)— 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.
RGC+ASQ (PTB)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
RGC+ASQ (PTB)— how much compute was used to train it?
Training consumed around 1.2 × 10¹⁶ FLOP, on hardware recorded as NVIDIA Titan V. 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.
RGC+ASQ (PTB)— what GPU do I need to run it?
None. This 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.
RGC+ASQ (PTB)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
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.