TeleChat-3B
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
- China Telecom
- Organisation type
- Industry
- Country
- China
- Published
- 1 April 2024
- Authors
- Zhongjiang He, Zihan Wang, Xinzhang Liu, Shixuan Liu, Yitong Yao, Yuyao Huang, Xuelong Li, Yongxiang Li, Zhonghao Che, Zhaoxi Zhang, Yan Wang, Xin Wang, Luwen Pu, Huinan Xu, Ruiyu Fang, Yu Zhao, Jie Zhang, Xiaomeng Huang, Zhilong Lu, Jiaxin Peng, Wenjun Zheng, Shiquan Wang, Bingkai Yang, Xuewei he, Zhuoru Jiang, Qiyi Xie, Yanhan Zhang, Zhongqiu Li, Lingling Shi, Weiwei Fu, Yin Zhang, Zilu Huang, S…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Chat, Question answering, Text summarization, Code generation, Translation
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
- 3B
- Training data
- tokens
Table 3
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 × 10²² FLOP
- How it was established
- Operation counting
80 nodes, each having 8 Nvidia A100 Sxm 40GB GPUs 6 FLOP / token / parameter * 3*10^9 parameters * 0.8 * 10^12 tokens = 1.44e+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 A100 SXM4 40 GB
- Chips used
- 640
- Power draw
- 506.2 kW
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.
- Record confidence
- Confident
- Citations
- 13
Sources
Where this record came from and when it was last checked.
- Reference
- TELECHAT TECHNICAL REPORT
- Last updated
- 25 May 2026
What the numbers mean
Background
TeleChat-3B was published by China Telecom, in China, in April 2024. It comes out of industry.
It works in Language, and is recorded as doing language modeling/generation, Chat, Question answering, Text summarization, Code generation, Translation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required around 1.4 × 10²² FLOP of arithmetic, on NVIDIA A100 SXM4 40 GB, which is a statement about the training budget rather than about inference.
Answers
TeleChat-3B — common questions
What is TeleChat-3B used for?
TeleChat-3B works in Language, and is recorded as handling language modeling/generation, Chat, Question answering, Text summarization, Code generation, Translation. 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 TeleChat-3B?
Around 1.4 × 10²² FLOP, on NVIDIA A100 SXM4 40 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.
What GPU do I need to run TeleChat-3B?
None. TeleChat-3B 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 TeleChat-3B open source?
No. TeleChat-3B has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does TeleChat-3B have?
TeleChat-3B has 3B 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 TeleChat-3B?
TeleChat-3B was published by China Telecom, based in China, categorised as industry.
When was TeleChat-3B released?
TeleChat-3B was published in April 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.