SparseOPT-175B
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
- Institute of Science and Technology Austria (ISTA),Neural Magic
- Organisation type
- Academia,Industry
- Country
- Austria, United States of America
- Published
- 2 January 2023
- Authors
- Elias Frantar, Dan Alistarh
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Question answering
- Base model
- OPT-175B
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
- 87.5B
- Training data
- tokens
- Epochs
- 1.67
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
- 1.6 × 10²³ FLOP
- How it was established
- Hardware
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 80 GB
- Chips used
- 1
- Wall-clock time
- 4 hours
- Power draw
- 439 W
"All pruning experiments are conducted on a single NVIDIA A100 GPU with 80GB of memory. In this setup, SparseGPT can fully sparsify the 175-billion-parameter models in approximately 4 hours."
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
- Open source
code is Apache 2.0 (but OPT, which you'd need to recreate this model, is non-commercial) https://github.com/IST-DASLab/sparsegpt/blob/master/opt.py
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
- Record confidence
- Confident
- Citations
- 1,267
- Benchmark data
- SparseOPT-175B
Sources
Where this record came from and when it was last checked.
- Reference
- SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
- Last updated
- 25 May 2026
What the numbers mean
What this model is
SparseOPT-175B was published by Institute of Science and Technology Austria (ISTA),Neural Magic, in Austria, in January 2023. academia,Industry is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling/generation, Question answering.
It is derived from OPT-175B rather than trained from scratch, which is the usual way a specialised model is produced.
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 1.6 × 10²³ FLOP, on NVIDIA A100 SXM4 80 GB. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
SparseOPT-175B — common questions
Who created SparseOPT-175B?
SparseOPT-175B was published by Institute of Science and Technology Austria (ISTA),Neural Magic, based in Austria, categorised as academia,Industry.
When was SparseOPT-175B released?
SparseOPT-175B was published in January 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.
What is SparseOPT-175B used for?
SparseOPT-175B works in Language, and is recorded as handling language modeling/generation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train SparseOPT-175B?
Around 1.6 × 10²³ FLOP, on NVIDIA A100 SXM4 80 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 SparseOPT-175B?
None. SparseOPT-175B 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 SparseOPT-175B open source?
No. SparseOPT-175B has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does SparseOPT-175B have?
SparseOPT-175B has 87.5B 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.
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.