Gopher (280B)
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
- DeepMind
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 8 December 2021
- Authors
- Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Question answering
- Approach
- Self-supervised learning
- Numerical format
- BF16
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
- 280B
- Training data
- 300,000,000,000 tokens
- Epochs
- 1
- Batch size
- 6,000,000
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across …
"We train all models for 300 billion tokens with a 2048 token context window, using the Adam (Kingma and Ba, 2014) optimiser." 1 token ~ 0.75 words
Table 1. "Furthermore, we increase Gopher’s batch size from three to six million tokens per batch during training"
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
- 6.3 × 10²³ FLOP
- How it was established
- Reported
Table A26 6.31E+08 Train PFLOPs
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
- 4,096
- Chip-hours
- 3,768,320
- Wall-clock time
- 920 hours (38.3 days)
- Hardware utilisation
- MFU 37.8%
- Power draw
- 3.7 MW
- Compute cost
- $640,617
"We trained Gopher for 920 hours in November and December 2020 in Google’s Georgia datacentre. The PUE of the datacenter at this time was 1.08; the net tCO2e per MWh in October 2020 was 0.33. Using an estimate of 283W drawn per chip, this leads to a total of 380 net tCO2e"
Compute used to train model (table A26): 6.31e23 Maximum possble compute based on GPU-hours at 100% utilization: 920 * 4096 * 3600 * 1.23e14 = 1.669e24 Therefore, we can calculate utilization: MFU = 6.31e23 / 1.669e24 = 0.3780
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
- 1,605
- Benchmark data
- Gopher (280B)
"These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority"
Sources
Where this record came from and when it was last checked.
- Reference
- "Scaling Language Models: Methods, Analysis & Insights from Training Gopher"
- Last updated
- 25 May 2026
What the numbers mean
Background
Gopher (280B) was published by DeepMind, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during December 2021. 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, Question answering.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Training it took a computation budget of roughly 6.3 × 10²³ FLOP, on hardware recorded as Google TPU v3. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 300,000,000,000 tokens of text.
Its inclusion criterion: sOTA improvement.
Answers
Gopher (280B) — common questions
Gopher (280B)— when was it released?
It was published in December 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Gopher (280B)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Gopher (280B)— how much compute was used to train it?
Training consumed around 6.3 × 10²³ FLOP, on hardware recorded as 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.
Gopher (280B)— 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.
Gopher (280B)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Gopher (280B)— how many parameters does it have?
It has a parameter count of 280B. Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher. These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority. Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit. We provide a holistic analysis of the training dataset and model's behaviour, covering the intersection of model scale with bias and toxicity. Finally we discuss the application of language models to AI safety and the mitigation of downstream harms. 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.
Gopher (280B)— who created it?
It was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
The other direction
Looking at it from the other side?
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