JetFire (GPT2-LARGE)
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
- Tsinghua University
- Organisation type
- Academia
- Country
- China
- Published
- 19 March 2024
- Authors
- Haocheng Xi, Yuxiang Chen, Kang Zhao, Kaijun Zheng, Jianfei Chen, Jun Zhu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation
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
- 774M
- Training data
- tokens
Table 4.
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 GeForce RTX 4090
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block Quantization
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
JetFire (GPT2-LARGE) was published by Tsinghua University, in China, in March 2024. It comes out of 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.
Answers
JetFire (GPT2-LARGE) — common questions
How many parameters does JetFire (GPT2-LARGE) have?
JetFire (GPT2-LARGE) has 774M parameters. Table 4. 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 JetFire (GPT2-LARGE)?
JetFire (GPT2-LARGE) was published by Tsinghua University, based in China, categorised as academia.
When was JetFire (GPT2-LARGE) released?
JetFire (GPT2-LARGE) was published in March 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.
What is JetFire (GPT2-LARGE) used for?
JetFire (GPT2-LARGE) works in Language, and is recorded as handling language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run JetFire (GPT2-LARGE)?
None. JetFire (GPT2-LARGE) 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 JetFire (GPT2-LARGE) open source?
The licensing for JetFire (GPT2-LARGE) was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.