$\infty$-former (SM)
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
- Universidade de Lisboa (ULisboa),DeepMind
- Organisation type
- Academia,Industry
- Country
- Portugal, United Kingdom of Great Britain and Northern Ireland
- Published
- 1 September 2021
- Authors
- Pedro Henrique Martins, Zita Marinho, André F. T. Martins
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation
- Base model
- GPT-2 (124M)
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
- 124M
- Training data
- 200,000,000 tokens
- Epochs
- 1
assuming same as the base model GPT-2 small
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 × 10²² FLOP
- How it was established
- Operation counting
- Fine-tuning compute
- 1.5 × 10¹⁷ FLOP
7.936 × 10^20 FLOP [base model compute estimation, "Speculative" confidence] + 1.488e+17 FLOP [fine-tune compute] = 7.937488e+20 FLOP
"To understand if long-term memories can be used to extend a pre-trained language model, we fine-tune GPT-2 small (Radford et al., 2019) on Wikitext103 (Merity et al., 2017) and a subset of PG-19 (Rae et al., 2019) containing the first 2,000 books (≈ 200 million tokens) of the training set" 6 FLOP / token / parameter * 124000000 parameters * 200000000 tokens * 1 epoch = 1.488e+17 FLOP
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 GeForce RTX 2080 Ti 11GB
- Chips used
- 1
- Power draw
- 277 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
- Open (non-commercial)
fine-tune code (this is a GPT-2 finetune), no clear license: https://github.com/deep-spin/infinite-former
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
- Citations
- 31
- Benchmark data
- $\infty$-former (SM)
Sources
Where this record came from and when it was last checked.
- Reference
- $\infty$-former: Infinite Memory Transformer
- Last updated
- 11 February 2026
What the numbers mean
Background
$\infty$-former (SM) was published by Universidade de Lisboa (ULisboa),DeepMind, in the country recorded as Portugal, during September 2021. The category the publisher falls under is academia,Industry.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation.
Its starting point was an existing base model, GPT-2 (124M). That is the usual way a specialised model is produced.
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 1.2 × 10²² FLOP, on hardware recorded as NVIDIA GeForce RTX 2080 Ti 11GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 200,000,000 tokens of text.
Answers
$\infty$-former (SM) — common questions
$\infty$-former (SM)— how many parameters does it have?
It has a parameter count of 124M. assuming same as the base model GPT-2 small. 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.
$\infty$-former (SM)— who created it?
It was published by Universidade de Lisboa (ULisboa),DeepMind, based in Portugal, an organisation categorised as academia,Industry.
$\infty$-former (SM)— when was it released?
It was published in September 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
$\infty$-former (SM)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation. 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.
$\infty$-former (SM)— how much compute was used to train it?
Training consumed around 1.2 × 10²² FLOP, on hardware recorded as 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.
$\infty$-former (SM)— 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.
$\infty$-former (SM)— 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.