MoE-Multi
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
- Jagiellonian University,Google Brain
- Organisation type
- Academia,Industry
- Country
- Poland, United States of America
- Published
- 23 January 2017
- Authors
- N Shazeer, A Mirhoseini, K Maziarz, A Davis
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Translation
- Numerical format
- FP32
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
- 8.7B
- Training data
- 87,000,000,000 tokens
- Epochs
- 10
- Batch size
- 1,365,333
Table 5 https://arxiv.org/abs/1701.06538
"We constructed a similar training set consisting of shuffled unique sentences from Google’s internal news corpus, totalling roughly 100 billion words" Assuming 100 words = 133 tokens
"Training was done synchronously on a cluster of up to 64 GPUs as described in section 3. Each training batch consisted of a set of sentence pairs containing roughly 16000 words per GPU." Although they appear to use word-level tokenization in other experiments, here they use subword tokens: "Similar to GNMT, to effectively deal with rare words, we used subword units (also known as “wordpieces") (Schuster & Nakajima, 2012) for inputs and outputs in our system." In total 64 GPUs * 16k words/GPU * …
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
- 9.4 × 10¹⁹ FLOP
- How it was established
- Hardware
12 days 64 NVIDIA K40 GPUs (see hardware data sheet for performance) 0.33 util rate
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA Tesla K40t
- Chips used
- 64
- Wall-clock time
- 288 hours (12 days)
- Power draw
- 32.9 kW
- Compute cost
- $3,874
12 days
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.
- Why it is tracked
- Highly cited,SOTA improvement,Historical significance
- Record confidence
- Confident
- Citations
- 4,587
"On large language modeling and machine translation benchmarks, these models achieve significantly better results than state-of-the-art at lower computational cost"
Sources
Where this record came from and when it was last checked.
- Reference
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
- Last updated
- 25 May 2026
What the numbers mean
About this model
MoE-Multi was published by Jagiellonian University,Google Brain, in Poland, in January 2017. The organisation is categorised as academia,Industry.
It works in Language, and is recorded as doing language modeling, Translation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Training it took roughly 9.4 × 10¹⁹ FLOP of computation, on NVIDIA Tesla K40t — a measure of what producing the model cost, not of how fast it answers.
Around 87,000,000,000 tokens went into training it.
It is tracked in the underlying dataset for one reason in particular: highly cited,SOTA improvement,Historical significance.
Answers
MoE-Multi — common questions
What GPU do I need to run MoE-Multi?
None. MoE-Multi 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 MoE-Multi open source?
No. MoE-Multi has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does MoE-Multi have?
MoE-Multi has 8.7B parameters. Table 5 https://arxiv.org/abs/1701.06538. 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 MoE-Multi?
MoE-Multi was published by Jagiellonian University,Google Brain, based in Poland, categorised as academia,Industry.
When was MoE-Multi released?
MoE-Multi was published in January 2017. 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 MoE-Multi used for?
MoE-Multi works in Language, and is recorded as handling language modeling, Translation. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train MoE-Multi?
Around 9.4 × 10¹⁹ FLOP, on NVIDIA Tesla K40t. 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.