MemSizer (language modeling)
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
- Meta AI,Chinese University of Hong Kong (CUHK)
- Organisation type
- Industry,Academia
- Country
- United States of America, Hong Kong
- Published
- 23 March 2022
- Authors
- Yizhe Zhang, Deng Cai
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
- 357M
- Training data
- 103,000,000 tokens
357M Table 3 "Following Kasai et al. (2021), we choose similar hyperparameters to prior work (Baevski and Auli, 2019; Fan et al., 2020): 32 layers, 8 heads, 128 head dimensions, 1024 model dimensions, 4096 fully connected dimensions and dropout (Srivastava et al., 2014) and layer dropout rates of 0.2. "
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
- 7.3 × 10¹⁸ FLOP
- How it was established
- Operation counting
6 FLOP / token / parameter * 357000000 parameters * 103000000 tokens * 1 epoch [assumed for lower bound] = 2.20626e+17 FLOP also to consider: 7.3 × 10^18 FLOP SOURCE: Impute based on Baevski and Auli 2019 "We generally follow the optimization method from Baevski and Auli (2019), with a slight modification for some hyperparameters including learning rate (we use 10−4), which shows better convergence. " NOTES: Probably correct within an order of 2 or so
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 (non-commercial)
code, no license: https://github.com/jcyk/memsizer wt103 train: https://github.com/jcyk/memsizer/blob/main/lm_wikitext-103.sh
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
- 6
- Benchmark data
- MemSizer
Sources
Where this record came from and when it was last checked.
- Reference
- Linearizing Transformer with Key-Value Memory
- Last updated
- 11 February 2026
What the numbers mean
What this model is
MemSizer (language modeling) was published by Meta AI,Chinese University of Hong Kong (CUHK), in the country recorded as United States of America, during March 2022. It comes out of an organisation categorised as industry,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Producing it required arithmetic totalling around 7.3 × 10¹⁸ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 103,000,000 tokens of text.
Answers
MemSizer (language modeling) — common questions
MemSizer (language modeling)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
MemSizer (language modeling)— how much compute was used to train it?
Training consumed around 7.3 × 10¹⁸ 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.
MemSizer (language modeling)— 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.
MemSizer (language modeling)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
MemSizer (language modeling)— how many parameters does it have?
It has a parameter count of 357M. 357M Table 3 "Following Kasai et al. (2021), we choose similar hyperparameters to prior work (Baevski and Auli, 2019; Fan et al., 2020): 32 layers, 8 heads, 128 head dimensions, 1024 model dimensions, 4096 fully connected dimensions and dropout (Srivastava et al., 2014) and layer dropout rates of 0.2. ". 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.
MemSizer (language modeling)— who created it?
It was published by Meta AI,Chinese University of Hong Kong (CUHK), based in United States of America, an organisation categorised as industry,Academia.
MemSizer (language modeling)— when was it released?
It was published in March 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.