RCTM
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
- University of Oxford
- Organisation type
- Academia
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 1 October 2013
- Authors
- Nal Kalchbrenner, Phil Blunsom
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Translation
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.
- Training data
- 4,500,000 tokens
"The English sentences contain about 4.1M words"
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
- 9.3 × 10¹⁵ FLOP
- How it was established
- Hardware
"The training of an RCTM takes about 15 hours on 3 multicore CPUs" Given the publication year, a rough estimate for the CPU performance is 16 FP32 per cycle, 4 cores, clock speed 4GHz, utilization of 0.3. 15*60*60*3*4*12*4000000000*0.3=9331200000000000=9.33e15
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chips used
- 3
- Wall-clock time
- 15 hours
The training of an RCTM takes about 15 hours on 3 multicore CPUs.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- Recurrent Continuous Translation Models
- Last updated
- 28 November 2025
What the numbers mean
What this model is
RCTM was published by University of Oxford, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during October 2013. The category the publisher falls under is academia.
It works in the domain of Language, and is recorded as performing the task of translation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Training it took a computation budget of roughly 9.3 × 10¹⁵ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 4,500,000 tokens of text.
Its inclusion criterion: highly cited.
Answers
RCTM — common questions
RCTM— 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.
RCTM— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
RCTM— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
RCTM— who created it?
It was published by University of Oxford, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as academia.
RCTM— when was it released?
It was published in October 2013. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
RCTM— what is it used for?
It works in the domain of Language, and is recorded as handling the task of translation. 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.
RCTM— how much compute was used to train it?
Training consumed around 9.3 × 10¹⁵ 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.
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.