Conditional Maximum Entropy Model (Gigaworld)
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 1 July 2009
- Authors
- Ryan Mcdonald, Mehryar Mohri, Nathan Silberman, Dan Walker, Gideon S. Mann
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
- 1M
- Training data
- 1,000,000,000 tokens
"roughly 1M parameters"
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 × 10¹⁸ FLOP
Number of epochs is unknown. Based on 18,598 CPU-hours with Intel Xeon 5500 processors (speculative), this would be up to 8e17 FP64 FLOP or 1.6e18 FP32 FLOP. With 60% utilization it's around 1e18 FLOP.
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chip-hours
- 18,598
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
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models
- Last updated
- 11 February 2026
What the numbers mean
Background
Conditional Maximum Entropy Model (Gigaworld) was published by Google, in the country recorded as United States of America, during July 2009. The publishing organisation is categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Producing it required arithmetic totalling around 1 × 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 1,000,000,000 tokens of text.
Answers
Conditional Maximum Entropy Model (Gigaworld) — common questions
Conditional Maximum Entropy Model (Gigaworld)— how much compute was used to train it?
Training consumed around 1 × 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.
Conditional Maximum Entropy Model (Gigaworld)— 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.
Conditional Maximum Entropy Model (Gigaworld)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Conditional Maximum Entropy Model (Gigaworld)— how many parameters does it have?
It has a parameter count of 1M. "roughly 1M 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.
Conditional Maximum Entropy Model (Gigaworld)— who created it?
It was published by Google, based in United States of America, an organisation categorised as industry.
Conditional Maximum Entropy Model (Gigaworld)— when was it released?
It was published in July 2009. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Conditional Maximum Entropy Model (Gigaworld)— 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.
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.