rTop-k(distributed setting)
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
- Stanford University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 21 May 2020
- Authors
- Leighton Pate Barnes, Huseyin A. Inan, Berivan Isik, Ayfer Ozgur
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
- 69M
- Training data
- 912,344 tokens
- Epochs
- 38
We adopted a 2-layer LSTM language model architec- ture with 1500 hidden units per layer Okay, let's break this down: - You have a 2-layer LSTM model - Each LSTM layer has 1500 hidden units - The vocabulary size is 10k For each LSTM layer: - There are 4 weight matrices (input, forget, output, cell state) per layer - Each weight matrix has dimensions (input size x 1500 hidden units) - Plus 1500 bias terms per matrix Layer 1: - Input size is 10k (vocab size) - Weight matrices: 4 * (10k * 150…
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
- 1.4 × 10¹⁶ FLOP
- How it was established
- Operation counting
6 FLOP / token / parameter * 69000000 parameters * 912344 tokens * 38 epochs = 1.4352996e+16 FLOP
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
- 71
- Benchmark data
- rTop-k(distributed setting)
Sources
Where this record came from and when it was last checked.
- Reference
- rTop-k: A Statistical Estimation Approach to Distributed SGD
- Last updated
- 25 May 2026
What the numbers mean
About this model
rTop-k(distributed setting) was published by Stanford University, in United States of America, in May 2020. The organisation is categorised as academia.
It works in Language, and is recorded as doing language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
The training run consumed about 1.4 × 10¹⁶ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Around 912,344 tokens went into training it.
Answers
rTop-k(distributed setting) — common questions
How much compute was used to train rTop-k(distributed setting)?
Around 1.4 × 10¹⁶ FLOP. 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 rTop-k(distributed setting)?
None. rTop-k(distributed setting) 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 rTop-k(distributed setting) open source?
No. rTop-k(distributed setting) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does rTop-k(distributed setting) have?
rTop-k(distributed setting) has 69M parameters. We adopted a 2-layer LSTM language model architec- ture with 1500 hidden units per layer Okay, let's break this down: - You have a 2-layer LSTM model - Each LSTM layer has 1500 hidden units - The vocabulary size is 10k For each LSTM layer: - There are 4 weight matrices (input, forget, output, cell state) per layer - Each weight matrix has dimensions (input size x 1500 hidden units) - Plus 1500 bias terms per matrix Layer 1: - Input size is 10k (vocab size) - Weight matrices: 4 * (10k * 1500) = 60 million parameters - Biases: 4 * 1500 = 6000 parameters Layer 2: - Input size is 1500 (output of layer 1) - Weight matrices: 4 * (1500 * 1500) = 9 million parameters - Biases: 4 * 1500 = 6000 parameters Total parameters = 60 million + 6000 + 9 million + 6000 = 69,012,000 So the total number of parameters in this 2-layer 1500-unit LSTM with 10k vocab size is approximately 69 million. 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 rTop-k(distributed setting)?
rTop-k(distributed setting) was published by Stanford University, based in United States of America, categorised as academia.
When was rTop-k(distributed setting) released?
rTop-k(distributed setting) was published in May 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 rTop-k(distributed setting) used for?
rTop-k(distributed setting) works in Language, and is recorded as handling language modeling. 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.