WeLM
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
- WeChat AI
- Organisation type
- Industry
- Country
- China
- Published
- 16 May 2023
- Authors
- Hui Su, Xiao Zhou, Houjin Yu, Xiaoyu Shen, Yuwen Chen, Zilin Zhu, Yang Yu, Jie Zhou
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Translation
- Approach
- Unsupervised
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
- 10B
- Training data
- 262,000,000,000 tokens
"WeLM is trained with 10B parameters”
from the paper "After all the above filtering process, our corpus contains 262B tokens”
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
- 2.5 × 10²² FLOP
- How it was established
- Hardware
"The largest model is trained on 128 A100-SXM4-40GB GPUs in about 24 days”, "All models are trained with FP16 mixed precision." 3.12e14 * 128 * 24 * 24 * 3600 * 0.3 = 2.48e22 (FLOP/GPU-s) * (GPU) * (days) * (h/day) * (sec/h) * (utilization assumption)
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
- 128
- Chip-hours
- 73,728
- Wall-clock time
- 576 hours (24 days)
- Power draw
- 102.0 kW
"The largest model is trained on 128 A100-SXM4-40GB GPUs in about 24 days”,
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
- 25
Sources
Where this record came from and when it was last checked.
- Reference
- WeLM: A Well-Read Pre-trained Language Model for Chinese
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
WeLM was published by WeChat AI, in China, in May 2023. It comes out of industry.
It works in Language, and is recorded as doing language modeling/generation, Translation.
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 2.5 × 10²² FLOP, on NVIDIA A100 SXM4 40 GB. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
The training set ran to roughly 262,000,000,000 tokens.
Answers
WeLM — common questions
What is WeLM used for?
WeLM works in Language, and is recorded as handling language modeling/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 WeLM?
Around 2.5 × 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 WeLM?
None. WeLM 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 WeLM open source?
The licensing for WeLM was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does WeLM have?
WeLM has 10B parameters. "WeLM is trained with 10B 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 WeLM?
WeLM was published by WeChat AI, based in China, categorised as industry.
When was WeLM released?
WeLM was published in May 2023. 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.