MegaScale (Production)
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
- ByteDance,Peking University
- Organisation type
- Industry,Academia
- Country
- China
- Published
- 23 February 2024
- Authors
- Ziheng Jiang, Haibin Lin, Yinmin Zhong, Qi Huang, Yangrui Chen, Zhi Zhang, Yanghua Peng, Xiang Li, Cong Xie, Shibiao Nong, Yulu Jia, Sun He, Hongmin Chen, Zhihao Bai, Qi Hou, Shipeng Yan, Ding Zhou, Yiyao Sheng, Zhuo Jiang, Haohan Xu, Haoran Wei, Zhang Zhang, Pengfei Nie, Leqi Zou, Sida Zhao, Liang Xiang, Zherui Liu, Zhe Li, Xiaoying Jia, Jianxi Ye, Xin Jin, Xin Liu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation
- 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
- 530B
- Training data
- tokens
Production run is stated to have "hundreds of billions of parameters". Since the authors also do a number of experiments with a 530B model, I speculate they've used 530B for the production model.
Speculative. Authors note production system was trained on "multi-trillions of tokens". This could refer to training for multiple epochs on the same 300B tokens used to train the 175B and 530B models outlined in more detail in the paper. Alternatively, it could refer to a larger dataset of perhaps 3-9 trillion 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
- 3.9 × 10²⁴ FLOP
- How it was established
- Other
Speculative. The model is stated to have trained for "several weeks". Assuming 530B parameters and "several" = 3, compute can be estimated from the 175B model's stated PFLOP/sec: 2166.3 aggregate PFlops/sec * 3 weeks * 7 days/week * 24 hours/day * 3600 seconds/hour = 3.9e+24. As an upper bound, say 8e+24.
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
- Chips used
- 12,288
- Wall-clock time
- 504 hours (21 days)
- Hardware utilisation
- MFU 48.0%
- Power draw
- 9.7 MW
- Compute cost
- $2,614,019
- Data centre
- ByteDance MegaScale training cluster
Speculative. Authors state "several weeks". For analysis, I've assumed this means around 3 weeks.
Figure 12 shows MFU, which is around 0.484 during stable periods, with short spikes to as low as 0.4. It seems like the overall average is likely minimally affected by the spikes, but I'll round down to 0.48
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
Code for MegaScale (also called veScale) training system are released under Apache Licence: https://github.com/volcengine/vescale The model itself is unreleased.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- SOTA improvement
- Record confidence
- Speculative
- Citations
- 302
Improves SOTA in FLOP utilization for distributed LLM training by 1.34X.
Sources
Where this record came from and when it was last checked.
- Reference
- MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
MegaScale (Production) was published by ByteDance,Peking University, in China, in February 2024. The organisation is categorised as industry,Academia.
It works in Language, and is recorded as doing language modeling/generation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The training run consumed about 3.9 × 10²⁴ FLOP, on NVIDIA A100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
MegaScale (Production) — common questions
What is MegaScale (Production) used for?
MegaScale (Production) works in Language, and is recorded as handling language modeling/generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train MegaScale (Production)?
Around 3.9 × 10²⁴ FLOP, on NVIDIA A100. 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 MegaScale (Production)?
None. MegaScale (Production) 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 MegaScale (Production) open source?
No. MegaScale (Production) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does MegaScale (Production) have?
MegaScale (Production) has 530B parameters. Production run is stated to have "hundreds of billions of parameters". Since the authors also do a number of experiments with a 530B model, I speculate they've used 530B for the production model. 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 MegaScale (Production)?
MegaScale (Production) was published by ByteDance,Peking University, based in China, categorised as industry,Academia.
When was MegaScale (Production) released?
MegaScale (Production) was published in February 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.