SNM-skip
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
- 3 December 2014
- Authors
- Noam Shazeer, Joris Pelemans, Ciprian Chelba
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
- 62B
- Training data
- 800,000,000 tokens
62B from Table 2
1B from 'Our experimental setup used the One Billion Word Benchmark corpus' from section 4.1 - 'Total number of training tokens is about 0.8 billion'
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 × 10²⁰ FLOP
- How it was established
- Operation counting
https://www.wolframalpha.com/input?i=0.8+billion+*+62+billion+*+6+FLOP
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
- SOTA improvement
- Record confidence
- Speculative
- Citations
- 14
'When using skip-gram features the models are able to match the state-of-the-art recurrent neural network (RNN) LMs; combining the two modeling techniques yields the best known result on the benchmark. ' - from abstract
Sources
Where this record came from and when it was last checked.
- Reference
- Skip-gram Language Modeling Using Sparse Non-negative Matrix Probability Estimation
- Last updated
- 28 November 2025
What the numbers mean
Background
SNM-skip was published by Google, in the country recorded as United States of America, during December 2014. It comes out of an organisation categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required arithmetic totalling around 3 × 10²⁰ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 800,000,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
SNM-skip — common questions
SNM-skip— 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.
SNM-skip— 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.
SNM-skip— how many parameters does it have?
It has a parameter count of 62B. 62B from Table 2. 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.
SNM-skip— who created it?
It was published by Google, based in United States of America, an organisation categorised as industry.
SNM-skip— when was it released?
It was published in December 2014. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
SNM-skip— 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.
SNM-skip— how much compute was used to train it?
Training consumed around 3 × 10²⁰ 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.