RNN 1000/5 + RT09 LM (NIST RT05)
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
- Brno University of Technology,Johns Hopkins University
- Organisation type
- Academia,Academia
- Country
- Czechia, United States of America
- Published
- 26 September 2010
- Authors
- T. Mikolov, M. Karafiat, L. Burget, J. Cernocky, and S. Khudanpur
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Speech recognition (ASR), Transcription
- Approach
- Supervised
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
- 77M
- Training data
- 5,400,000 tokens
- Epochs
- 20
"The acoustic HMMs are based on cross-word tied-states triphones trained discriminatively using MPE criteria. Feature extraction use 13 Mel-PLP’s features with deltas, double and triple deltas reduced by HLDA to 39-dimension feature vector" RNN 1000/5 indicates that the model has a 1000 units in the hidden layer and merges words that occur less than 5 times in the training set. Section 4.2 in this paper appears to indicate that NIST RT05 has a vocabulary of 50k words: https://www.fit.vut.cz/pe…
"Table 4: Comparison of very large back-off LMs and RNN LMs trained only on limited in-domain data (5.4M 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
- 5 × 10¹⁶ FLOP
- How it was established
- Operation counting
"Convergence is usually achieved after 10-20 epochs." Assuming a backward-forward ratio of 2:1, since this is a shallow network 6 * 77039000 * 5.4M * 20 = 5.00e16
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 1,200 hours (50 days)
The biggest individual models evaluated on WSJ (probably RNN 250/2 at 27M parameters) took about 2.1e16 FLOPs to train, and training took "several weeks". Assuming a linear scaleup and several = about 3: 3 weeks * 7 days/week * 24 hr/day * 5e16/2.1e16 = 1,200 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
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
- Likely
- Citations
- 6,038
Sources
Where this record came from and when it was last checked.
- Reference
- Recurrent neural network based language model
- Last updated
- 11 February 2026
What the numbers mean
Background
RNN 1000/5 + RT09 LM (NIST RT05) was published by Brno University of Technology,Johns Hopkins University, in Czechia, in September 2010. academia,Academia is the category the publisher falls under.
It works in Speech, and is recorded as doing speech recognition (ASR), Transcription.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required around 5 × 10¹⁶ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
Around 5,400,000 tokens went into training it.
Its inclusion criterion is highly cited.
Answers
RNN 1000/5 + RT09 LM (NIST RT05) — common questions
What GPU do I need to run RNN 1000/5 + RT09 LM (NIST RT05)?
None. RNN 1000/5 + RT09 LM (NIST RT05) 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 RNN 1000/5 + RT09 LM (NIST RT05) open source?
No. RNN 1000/5 + RT09 LM (NIST RT05) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does RNN 1000/5 + RT09 LM (NIST RT05) have?
RNN 1000/5 + RT09 LM (NIST RT05) has 77M parameters. "The acoustic HMMs are based on cross-word tied-states triphones trained discriminatively using MPE criteria. Feature extraction use 13 Mel-PLP’s features with deltas, double and triple deltas reduced by HLDA to 39-dimension feature vector" RNN 1000/5 indicates that the model has a 1000 units in the hidden layer and merges words that occur less than 5 times in the training set. Section 4.2 in this paper appears to indicate that NIST RT05 has a vocabulary of 50k words: https://www.fit.vut.cz/person/imikolov/public/rnnlm/char.pdf Using this dataset of english word frequencies ... https://www.kaggle.com/datasets/rtatman/english-word-frequency ... we can estimate that the number of words in the final vocabulary would have been around 38k when merging with threshold 5: https://colab.research.google.com/drive/1K5qH0EqXtFwTLESNtp4oelCM28GpGXt6?usp=sharing Thus the model in question would have: (39 + 1000 + 38k) * 1000 + 1000 * 38k = 77,039,000 parameters The RT09 LM they interpolate with appears to be a 4-gram model. In one sense, n-gram models have parameters roughly equal to the number of unique n-grams in the training data. However, these aren't parameters in the same sense as neural networks as they are accessed as a lookup table, so on each forward pass only one parameter is active. I choose not to include these parameters in our count. 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.
Who created RNN 1000/5 + RT09 LM (NIST RT05)?
RNN 1000/5 + RT09 LM (NIST RT05) was published by Brno University of Technology,Johns Hopkins University, based in Czechia, categorised as academia,Academia.
When was RNN 1000/5 + RT09 LM (NIST RT05) released?
RNN 1000/5 + RT09 LM (NIST RT05) was published in September 2010. 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 RNN 1000/5 + RT09 LM (NIST RT05) used for?
RNN 1000/5 + RT09 LM (NIST RT05) works in Speech, and is recorded as handling speech recognition (ASR), Transcription. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train RNN 1000/5 + RT09 LM (NIST RT05)?
Around 5 × 10¹⁶ 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.