GPT-2 (fine-tuned with HYDRA)
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
- University of California San Diego
- Organisation type
- Academia
- Country
- United States of America
- Published
- 16 October 2021
- Authors
- Kabir Nagrecha, Arun Kumar
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation
- Base model
- GPT-2 (1.5B)
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
- 1.5B
- Training data
- tokens
- Epochs
- 1
They don't actually say which GPT-2 version they use, but it appears to be XL (1.5B params), since Table 1 shows zero-shot accuracy of 18.34 on WikiText-2, which is what the 1.5B version got.
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
- 1.9 × 10²¹ FLOP
- How it was established
- Operation counting
- Fine-tuning compute
- 1.8 × 10¹⁶ FLOP
They fine-tuned GPT-2 (1.5B) for one epoch on WikiText-2. GPT-2 itself used 1.92e21 FLOP, and here we see 6 * 2M * 1.54B = 1.848e16 fine-tuning FLOP (which has almost no impact on total FLOP).
They fine-tuned GPT-2 (1.5B) for one epoch on WikiText-2. GPT-2 itself used 1.92e21 FLOP, and here we see 6 * 2M * 1.54B = 1.848e16 fine-tuning FLOP (which has almost no impact on total FLOP).
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
- 6
- Benchmark data
- GPT-2 (fine-tuned with HYDRA)
Sources
Where this record came from and when it was last checked.
- Reference
- Hydra: A System for Large Multi-Model Deep Learning
- Last updated
- 25 May 2026
What the numbers mean
What this model is
GPT-2 (fine-tuned with HYDRA) was published by University of California San Diego, in United States of America, in October 2021. It comes out of academia.
It works in Language, and is recorded as doing language modeling/generation.
It is derived from GPT-2 (1.5B) rather than trained from scratch, which is the usual way a specialised model is produced.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The training run consumed about 1.9 × 10²¹ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
GPT-2 (fine-tuned with HYDRA) — common questions
How much compute was used to train GPT-2 (fine-tuned with HYDRA)?
Around 1.9 × 10²¹ FLOP. 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 GPT-2 (fine-tuned with HYDRA)?
None. GPT-2 (fine-tuned with HYDRA) 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 GPT-2 (fine-tuned with HYDRA) open source?
No. GPT-2 (fine-tuned with HYDRA) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does GPT-2 (fine-tuned with HYDRA) have?
GPT-2 (fine-tuned with HYDRA) has 1.5B parameters. They don't actually say which GPT-2 version they use, but it appears to be XL (1.5B params), since Table 1 shows zero-shot accuracy of 18.34 on WikiText-2, which is what the 1.5B version got. 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 GPT-2 (fine-tuned with HYDRA)?
GPT-2 (fine-tuned with HYDRA) was published by University of California San Diego, based in United States of America, categorised as academia.
When was GPT-2 (fine-tuned with HYDRA) released?
GPT-2 (fine-tuned with HYDRA) was published in October 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 GPT-2 (fine-tuned with HYDRA) used for?
GPT-2 (fine-tuned with HYDRA) 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.
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.