QT-Opt
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,University of California (UC) Berkeley
- Organisation type
- Industry,Academia
- Country
- United States of America
- Published
- 27 June 2018
- Authors
- Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, Sergey Levine
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Robotics, Vision
- Task
- Robotic manipulation
- Approach
- Reinforcement 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
- 1.2M
- Training data
- 11,600,000 tokens
- Epochs
- 80
"The Q-function Qθ(s, a) is represented in our system by a large convolutional neural network with 1.2M parameters"
Observations take up 4TB of disk space, and the input space is a 472x472 RGB image. Assuming 24 bit depth color (8 bits per channel), that suggests 472 * 472 * 3 * 8 bits = 668.352 kB per image (this could be off by a factor of 2 depending on actual bit depth) 4 TB / 668.352 kB = 5,984,870 images; around 10 per grasp attempt. 15M gradient steps with batchsize 32 implies: 15M steps * 32 images/step * 1/5984870 images ~= each image seen 80 times
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
- 1.4 × 10¹⁹ FLOP
- How it was established
- Hardware
"We distribute training across 10 GPUs, using asynchronous SGD with momentum... This system allows us to train the Q-function at 40 steps per second with a batch size of 32 across 10 NVIDIA P100 GPUs." "We found empirically that a large number of gradient steps (up to 15M) were needed to train an effective Q-function..." 15M steps * 0.025 seconds/step * 9.30E+12 FLOP/sec/GPU * 0.4 utilization * 10 GPU = 1.395E+19
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 P100
- Wall-clock time
- 104 hours
- Compute cost
- $1,318
"We distribute training across 10 GPUs, using asynchronous SGD with momentum... This system allows us to train the Q-function at 40 steps per second with a batch size of 32 across 10 NVIDIA P100 GPUs." "We found empirically that a large number of gradient steps (up to 15M) were needed to train an effective Q-function..." 15M steps * 0.025 seconds/step * 1/3600 hours/second = 104.2 hours
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.
- Record confidence
- Likely
- Citations
- 1,692
Sources
Where this record came from and when it was last checked.
- Reference
- QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
- Last updated
- 25 May 2026
What the numbers mean
About this model
QT-Opt was published by Google Brain,University of California (UC) Berkeley, in United States of America, in June 2018. The organisation is categorised as industry,Academia.
It works in Robotics, Vision, and is recorded as doing robotic manipulation.
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 1.4 × 10¹⁹ FLOP, on NVIDIA P100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Around 11,600,000 tokens went into training it.
Answers
QT-Opt — common questions
How many parameters does QT-Opt have?
QT-Opt has 1.2M parameters. "The Q-function Qθ(s, a) is represented in our system by a large convolutional neural network with 1.2M parameters". 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 QT-Opt?
QT-Opt was published by Google Brain,University of California (UC) Berkeley, based in United States of America, categorised as industry,Academia.
When was QT-Opt released?
QT-Opt was published in June 2018. 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 QT-Opt used for?
QT-Opt works in Robotics, Vision, and is recorded as handling robotic manipulation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train QT-Opt?
Around 1.4 × 10¹⁹ FLOP, on NVIDIA P100. 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 QT-Opt?
None. QT-Opt 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 QT-Opt open source?
No. QT-Opt has not had its weights published, so it exists only as a service controlled by its owner.
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.