VD-RHN
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
- ETH Zurich,IDSIA
- Organisation type
- Academia,Academia
- Country
- Switzerland
- Published
- 12 July 2016
- Authors
- Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, Jürgen Schmidhuber
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
- 32M
- Training data
- 929,000 tokens
- Epochs
- 20
"To examine the effect of recurrence depth we train RHNs with fixed total parameters (32 M)"
"Batch size was fixed to 20, sequence length for truncated backpropagation to 35, learning rate to 0.2, learning rate decay to 1.02 starting at 20 epochs, weight decay to 1e-7 and maximum gradient norm to 10."
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
- 3.6 × 10¹⁵ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 32000000 parameters * 929000 tokens * 20 epochs = 3.56736e+15 FLOP
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 source
MIT for code: https://github.com/jzilly/RecurrentHighwayNetworks
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 493
- Benchmark data
- VD-RHN
Sources
Where this record came from and when it was last checked.
- Reference
- Recurrent Highway Networks
- Last updated
- 28 November 2025
What the numbers mean
About this model
VD-RHN was published by ETH Zurich,IDSIA, in Switzerland, in July 2016. The organisation is categorised as academia,Academia.
It works in Language, and is recorded as doing language modeling.
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 roughly 3.6 × 10¹⁵ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
Around 929,000 tokens went into training it.
Answers
VD-RHN — common questions
Is VD-RHN open source?
No. VD-RHN has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does VD-RHN have?
VD-RHN has 32M parameters. "To examine the effect of recurrence depth we train RHNs with fixed total parameters (32 M)". 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 VD-RHN?
VD-RHN was published by ETH Zurich,IDSIA, based in Switzerland, categorised as academia,Academia.
When was VD-RHN released?
VD-RHN was published in July 2016. 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 VD-RHN used for?
VD-RHN 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 VD-RHN?
Around 3.6 × 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.
What GPU do I need to run VD-RHN?
None. VD-RHN 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.