Memformer (4 encoder + 16 decoder)
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
- UC Davis,Westlake University,Facebook AI
- Organisation type
- Academia,Academia,Industry
- Country
- United States of America, China
- Published
- 14 October 2020
- Authors
- Qingyang Wu, Zhenzhong Lan, Kun Qian, Jing Gu, Alborz Geramifard, Zhou Yu
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
- 76.2M
- Training data
- 103,000,000 tokens
- Epochs
- 11.93
batch size 128 steps: 150000 sequence length: 128-1024
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 × 10¹⁹ FLOP
- How it was established
- Hardware
32400000000000 FLOP / second / GPU * 4 GPUs * 96 hours * 3600 sec / hour * 0.3 [assumed utilization] = 1.3436928e+19 FLOP time could be given for all language modeling not just for this model -> likely to be an upper bound
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 Tesla V100 DGXS 16 GB,NVIDIA GeForce RTX 2080 Ti 11GB
- Chips used
- 4
- Wall-clock time
- 96 hours
"We trained our model on NVIDIA V100 16GB and 2080Ti 11GB. <..> The training for language modeling took approximately four days on four GPUs." 4 days = 96 hours
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
- 77
- Benchmark data
- Memformer (4 encoder + 16 decoder)
Sources
Where this record came from and when it was last checked.
- Reference
- Memformer: A Memory-Augmented Transformer for Sequence Modeling
- Last updated
- 25 May 2026
What the numbers mean
About this model
Memformer (4 encoder + 16 decoder) was published by UC Davis,Westlake University,Facebook AI, in United States of America, in October 2020. It comes out of academia,Academia,Industry.
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.
How it was trained
The training run consumed about 1.2 × 10¹⁹ FLOP, on NVIDIA Tesla V100 DGXS 16 GB,NVIDIA GeForce RTX 2080 Ti 11GB. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
The training set ran to roughly 103,000,000 tokens.
Answers
Memformer (4 encoder + 16 decoder) — common questions
Who created Memformer (4 encoder + 16 decoder)?
Memformer (4 encoder + 16 decoder) was published by UC Davis,Westlake University,Facebook AI, based in United States of America, categorised as academia,Academia,Industry.
When was Memformer (4 encoder + 16 decoder) released?
Memformer (4 encoder + 16 decoder) was published in October 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 Memformer (4 encoder + 16 decoder) used for?
Memformer (4 encoder + 16 decoder) works in Language, and is recorded as handling language modeling. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
How much compute was used to train Memformer (4 encoder + 16 decoder)?
Around 1.2 × 10¹⁹ FLOP, on NVIDIA Tesla V100 DGXS 16 GB,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 Memformer (4 encoder + 16 decoder)?
None. Memformer (4 encoder + 16 decoder) 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 Memformer (4 encoder + 16 decoder) open source?
No. Memformer (4 encoder + 16 decoder) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Memformer (4 encoder + 16 decoder) have?
Memformer (4 encoder + 16 decoder) has 76.2M 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.
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.