Hierarchical LM
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.
- Published
- 6 January 2005
- Authors
- Frederic Morin, Yoshua Bengio
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.
- Training data
- 900,000 tokens
- Epochs
- 30
"The corpus has 1,105,515 occurrences of words, split into 3 sets: 900,000 for training, 100,000 for validation (model selection), and 105,515 for testing"
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 × 10¹⁴ FLOP
- How it was established
- Hardware
"The computations were performed on Athlon processors with a 1.2 GHz clock" FP32 per cycle: 4 ("The bottom line is that the Athlon is capable of delivering as many as four 32-bit, single-precision floating-point results per clock cycle", https://www.pctechguide.com/amd-technology/amd-athlon) Training time per epoch: 1609s (table 1) Epochs: 30 "Training is performed over about 20 to 30 epochs according to validation set perplexity (early stopping)." Assumed utilization: 0.5 Compute estimate: 0.…
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
- 1
- Wall-clock time
- 13 hours
Training time per epoch: 1609s (table 1) Total training time 30*1609/60/60=13.408h
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Why it is tracked
- Highly cited
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Hierarchical Probabilistic Neural Network Language Model
- Last updated
- 28 November 2025
What the numbers mean
Background
Hierarchical LM was published by its authors, in January 2005.
It works in Language, and is recorded as doing language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Training it took roughly 1.2 × 10¹⁴ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
The training set ran to roughly 900,000 tokens.
Its inclusion criterion is highly cited.
Answers
Hierarchical LM — common questions
Is Hierarchical LM open source?
The licensing for Hierarchical LM was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Hierarchical LM have?
No parameter count has been published for Hierarchical LM, which is why no memory or speed figure appears on this page.
When was Hierarchical LM released?
Hierarchical LM was published in January 2005. 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 Hierarchical LM used for?
Hierarchical LM 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 Hierarchical LM?
Around 1.2 × 10¹⁴ 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.
What GPU do I need to run Hierarchical LM?
None. Hierarchical LM 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.
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.