ERNIE 3.0 Titan
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,Peng Cheng Laboratory
- Organisation type
- Industry,Academia
- Country
- China
- Published
- 23 December 2021
- Authors
- Shuohuan Wang, Yu Sun, Yang Xiang, Zhihua Wu, Siyu Ding, Weibao Gong, Shikun Feng
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Language modeling/generation, Relation extraction, Sentiment classification, Text classification
- Numerical format
- FP16
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
- 260B
- Training data
- 668,000,000,000 tokens
- Batch size
- 1,048,576
"[We] developed... distributed training technology, including fine-grained parallelism, heterogeneous hardware-aware training, and fault tolerance mechanism to train the 260B model on both Nvidia V100 GPU and Ascend 910 NPU clusters." See also: https://twitter.com/BaiduResearch/status/1468633977242243078?t=6q4zuLNdTSc4GUBe9OM5Aw&s=19
"To ensure the success of the pre-training of ERNIE 3.0 Titan, we utilize the ERNIE 3.0 Corpus [ 2 ], a large-scale, wide-variety, and high-quality Chinese text corpora amounting to 4TB" Assuming 167M words/tokens per GB
"The maximum sequence length of context and the memory length of language generation is 512 and 128, respectively" In table 1, they use a global batch size of 512 when data parallelism is "1" and 2048 when DP is "4". Not sure I fully understand this part but I guess they'd use parallelism as much as possible given how they talk about it. 2048 * 512 = 1048576.
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 × 10²⁴ FLOP
- How it was established
- Operation counting
The paper suggests that ERNIE 3.0 Titan uses more compute than GPT-3. This is consistent with the 6ND approximation. C = 6ND = 6 (FLOP/param/token) * (260B params) * (668B tokens) = 1.0421*10^24 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,Huawei Ascend 910
- Chips used
- 1,920
- Data centre
- Peng Cheng Cloud Brain II, Paper on Ernie 3.0
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
- Hosted access (no API)
- Training code
- Unreleased
The Ernie 3.0 Titan model was used in Ernie bot. Today, ERNIE has been widely deployed across finance, healthcare, insurance, equity, Internet, logistics, and other fields. http://research.baidu.com/Blog/index-view?id=165
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Foundation model
- Yes
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 87
"Empirical results show that the ERNIE 3.0 Titan outperforms the state-of-the-art models on 68 NLP datasets."
Sources
Where this record came from and when it was last checked.
- Reference
- ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
- Last updated
- 25 May 2026
What the numbers mean
Background
ERNIE 3.0 Titan was published by Baidu,Peng Cheng Laboratory, in China, in December 2021. It comes out of industry,Academia.
It works in Language, and is recorded as doing language modeling, Language modeling/generation, Relation extraction, Sentiment classification, Text classification.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Producing it required around 1 × 10²⁴ FLOP of arithmetic, on NVIDIA Tesla V100 DGXS 32 GB,Huawei Ascend 910, which is a statement about the training budget rather than about inference.
Around 668,000,000,000 tokens went into training it.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
ERNIE 3.0 Titan — common questions
What GPU do I need to run ERNIE 3.0 Titan?
None. ERNIE 3.0 Titan 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 ERNIE 3.0 Titan open source?
No. ERNIE 3.0 Titan has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does ERNIE 3.0 Titan have?
ERNIE 3.0 Titan has 260B parameters. "[We] developed... distributed training technology, including fine-grained parallelism, heterogeneous hardware-aware training, and fault tolerance mechanism to train the 260B model on both Nvidia V100 GPU and Ascend 910 NPU clusters." See also: https://twitter.com/BaiduResearch/status/1468633977242243078?t=6q4zuLNdTSc4GUBe9OM5Aw&s=19. 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 ERNIE 3.0 Titan?
ERNIE 3.0 Titan was published by Baidu,Peng Cheng Laboratory, based in China, categorised as industry,Academia.
When was ERNIE 3.0 Titan released?
ERNIE 3.0 Titan 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.
What is ERNIE 3.0 Titan used for?
ERNIE 3.0 Titan works in Language, and is recorded as handling language modeling, Language modeling/generation, Relation extraction, Sentiment classification, Text classification. 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 ERNIE 3.0 Titan?
Around 1 × 10²⁴ FLOP, on NVIDIA Tesla V100 DGXS 32 GB,Huawei Ascend 910. 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.
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.