DeepSpeech2 (English)
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
- Baidu Research - Silicon Valley AI Lab
- Organisation type
- Industry
- Country
- United States of America
- Published
- 8 December 2015
- Authors
- Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich Elsen, Jesse Engel, Linxi Fan, Christopher Fougner, Tony Han, Awni Hannun, Billy Jun, Patrick LeGresley, Libby Lin, Sharan Narang, Andrew Ng, Sherjil Ozair, Ryan Prenger, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Yi Wan…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Speech recognition (ASR)
- 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
- 38M
- Training data
- 716,400,000 tokens
All networks have 38 million parameters.
"Our English speech system is trained on 11,940 hours of speech, while the Mandarin system is trained on 9,400 hours." 11,940 * 13,680 = 163339200
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
- 2.6 × 10¹⁹ FLOP
- How it was established
- Operation counting,Third-party estimation
1 timestep = (1280 hidden units)^2 * (7 RNN layers * 4 matrices for bidirectional + 2 DNN layers) * (2 for doubling parameters from 36M to 72M) = 98 MFLOP 20 epochs * 12,000 hours * 3600 seconds/hour * 50 samples/sec * 98 MFLOP * 3 add-multiply * 2 backprop = 26,000 PF = 0.30 pfs-days See also AI and Compute by Dario Amodei and OpenAI https://openai.com/research/ai-and-compute
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 GeForce GTX TITAN X
- Chips used
- 16
- Chip-hours
- 1,920
- Wall-clock time
- 120 hours
- Hardware utilisation
- HFU 46.7%
- Power draw
- 8.5 kW
- Compute cost
- $214
"5 days" from AI and Compute https://openai.com/index/ai-and-compute/
"Overall the system sustains approximately 50 teraFLOP/second when training on 16 GPUs. This amounts to 3 teraFLOP/second per GPU which is about 50% of peak theoretical performance" HFU = (50/16) TFLOP / 6.69 TFLOP = 0.4670
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Record confidence
- Confident
- Citations
- 3,150
Sources
Where this record came from and when it was last checked.
- Reference
- Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
- Last updated
- 25 May 2026
What the numbers mean
About this model
DeepSpeech2 (English) was published by Baidu Research - Silicon Valley AI Lab, in the country recorded as United States of America, during December 2015. The category the publisher falls under is industry.
It works in the domain of Speech, and is recorded as performing the task of speech recognition (ASR).
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 2.6 × 10¹⁹ FLOP, on hardware recorded as NVIDIA GeForce GTX TITAN X. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 716,400,000 tokens of text.
Answers
DeepSpeech2 (English) — common questions
DeepSpeech2 (English)— who created it?
It was published by Baidu Research - Silicon Valley AI Lab, based in United States of America, an organisation categorised as industry.
DeepSpeech2 (English)— when was it released?
It was published in December 2015. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
DeepSpeech2 (English)— what is it used for?
It works in the domain of Speech, and is recorded as handling the task of speech recognition (ASR). 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.
DeepSpeech2 (English)— how much compute was used to train it?
Training consumed around 2.6 × 10¹⁹ FLOP, on hardware recorded as NVIDIA GeForce GTX TITAN X. 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.
DeepSpeech2 (English)— 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.
DeepSpeech2 (English)— 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.
DeepSpeech2 (English)— how many parameters does it have?
It has a parameter count of 38M. All networks have 38 million 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.
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.