Meena
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
- Google Brain
- Organisation type
- Industry
- Country
- United States of America
- Published
- 28 January 2020
- Authors
- Dongling Xiao, Han Zhang, Yukun Li, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Text autocompletion, Chat
- Approach
- Self-supervised learning
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
- 2.6B
- Training data
- 53,333,333,333 tokens
- Epochs
- 164
- Batch size
- 82,655
"We present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network is simply trained to minimize perplexity of the next token."
"The final Meena dataset contains 341GB of text (40B words)" Converting from GB to words yields 6.8e10, which is in the same OOM
61B tokens over 738k training steps, or 82655 tokens per batch on average. Not certain about warmup, etc
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
- 1.1 × 10²³ FLOP
- How it was established
- Hardware,Operation counting,Third-party estimation
https://arxiv.org/ftp/arxiv/papers/2104/2104.10350.pdf Table 4 In the paper: "We trained our best model for 30 days on a TPUv3 Pod (2,048 TPU cores) on the Meena dataset containing 40B words (or 61B BPE tokens) [...] by the end of training, the model had traversed the full training set 164 times (or epochs) and observed a total of about 10T tokens" Hardware: 30 * 24 * 3600 * (2048/2) * 1.23e14 * 0.3 = 9.794e22 Ops counting: 6 * 10T * 2.6B = 1.56E23 Geometric mean: sqrt(9.79e22*1.56E23) = 1.24e…
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
- Google TPU v3
- Chips used
- 1,024
- Chip-hours
- 737,280
- Wall-clock time
- 720 hours (30 days)
- Hardware utilisation
- HFU 34.3%
- Power draw
- 942.7 kW
- Compute cost
- $214,810
We trained our best model for 30 days on a TPUv3 Pod (2,048 TPU cores)
Per https://arxiv.org/ftp/arxiv/papers/2104/2104.10350.pdf 1.12e23 FLOPs used 1024 TPUv3s for 30 days: 30 * 24 * 3600 * 1024 * 1.23e14 = 3.2647e23 1.12e23 / 3.2647e23 HFU = 0.3431
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
- 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
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 1,020
"We also propose a human evaluation metric called Sensibleness and Specificity Average (SSA)... the full version of Meena (with a filtering mechanism and tuned decoding) scores 79% SSA, 23% higher in absolute SSA than the existing chatbots we evaluated"
Sources
Where this record came from and when it was last checked.
- Reference
- Towards a Human-like Open-Domain Chatbot
- Last updated
- 25 May 2026
What the numbers mean
About this model
Meena was published by Google Brain, in United States of America, in January 2020. It comes out of industry.
It works in Language, and is recorded as doing text autocompletion, Chat.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
The training run consumed about 1.1 × 10²³ FLOP, on Google TPU v3. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Around 53,333,333,333 tokens went into training it.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Meena — common questions
Who created Meena?
Meena was published by Google Brain, based in United States of America, categorised as industry.
When was Meena released?
Meena was published in January 2020. 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 Meena used for?
Meena works in Language, and is recorded as handling text autocompletion, Chat. 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 Meena?
Around 1.1 × 10²³ FLOP, on Google TPU v3. 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 Meena?
None. Meena 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 Meena open source?
No. Meena has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Meena have?
Meena has 2.6B parameters. "We present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network is simply trained to minimize perplexity of the next token.". 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.
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.