Turing-NLG
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
- Microsoft
- Organisation type
- Industry
- Country
- United States of America
- Published
- 13 February 2020
- Authors
- Corby Rosset
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Text autocompletion, Language generation, Text summarization
- 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
- 17B
- Training data
- 46,400,000,000 tokens
- Epochs
- 3.39
- Batch size
- 524,288
Authors say they pretrain on the same data as for Megatron-LM. From the Megatron-LM paper: https://arxiv.org/pdf/1909.08053.pdf "The resulting aggregate corpus contains 174 GB of deduplicated text." 174GB * 2e8words/GB = 3.48e10 words 3.48e10 words (if english) *4/3 = 46400000000 tokens confidence - likely
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
- 1.6 × 10²² FLOP
- How it was established
- Third-party estimation,Operation counting
source: https://lair.lighton.ai/akronomicon/ 157 PF-days * 3600 * 24 * 10^15 = 1.35648e+22 archived: https://github.com/lightonai/akronomicon/tree/main/akrodb 6ND=6*17000000000*46400000000=4.7328e+21 (confidence regarding dataset size - likely) Authors of "AI and Memory Wall" (https://github.com/amirgholami/ai_and_memory_wall) estimated model's training compute as 28,000,000 PFLOP = 2.8*10^22 FLOP
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 V100 DGXS 32 GB
- Chips used
- 256
- Power draw
- 130.9 kW
- Compute cost
- $51,660
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.
- Why it is tracked
- SOTA improvement
- Record confidence
- Likely
- Citations
- 114
- Benchmark data
- Turing-NLG
from paper: "Turing Natural Language Generation (T-NLG) is a 17 billion parameter language model by Microsoft that outperforms the state of the art on many downstream NLP tasks"
Sources
Where this record came from and when it was last checked.
- Reference
- Turing-NLG: A 17-billion-parameter language model by Microsoft
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Turing-NLG was published by Microsoft, in the country recorded as United States of America, during February 2020. It comes out of an organisation categorised as industry.
It works in the domain of Language, and is recorded as performing the task of text autocompletion, Language generation, Text summarization.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Producing it required arithmetic totalling around 1.6 × 10²² FLOP, on hardware recorded as NVIDIA Tesla V100 DGXS 32 GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 46,400,000,000 tokens of text.
The reason it appears in this catalogue at all: sOTA improvement.
Answers
Turing-NLG — common questions
Turing-NLG— how many parameters does it have?
It has a parameter count of 17B. 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.
Turing-NLG— who created it?
It was published by Microsoft, based in United States of America, an organisation categorised as industry.
Turing-NLG— when was it released?
It was published in February 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.
Turing-NLG— what is it used for?
It works in the domain of Language, and is recorded as handling the task of text autocompletion, Language generation, Text summarization. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Turing-NLG— how much compute was used to train it?
Training consumed around 1.6 × 10²² FLOP, on hardware recorded as NVIDIA Tesla V100 DGXS 32 GB. 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.
Turing-NLG— 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.
Turing-NLG— is it open source?
No. Its weights have not been 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.