Papers › Training a Large Video Model on a Single Machine in a Day
Training a Large Video Model on a Single Machine in a Day
Yue Zhao, Philipp Krähenbühl
Videos are big, complex to pre-process, and slow to train on. State-of-the-art large-scale video models are trained on clusters of 32 or more GPUs for several days. As a consequence, academia largely ceded the training of large video models to industry. In this paper, we show how to still train a state-of-the-art video model on a single machine with eight consumer-grade GPUs in a day. We identify three bottlenecks, IO, CPU, and GPU computation, and optimize each. The result is a highly efficient video training pipeline. For comparable architectures, our pipeline achieves higher accuracies with 1/8 of the computation compared to prior work. Code is available at https://github.com/zhaoyue-zephyrus/AVION.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Recognition | EPIC-KITCHENS-100 | Avion (ViT-L) | Action@1 | 54.4 | #3 of 32 | Archive leaderboard | report |
| Action Recognition | EPIC-KITCHENS-100 | Avion (ViT-L) | Noun@1 | 65.4 | #3 of 32 | Archive leaderboard | report |
| Action Recognition | EPIC-KITCHENS-100 | Avion (ViT-L) | Verb@1 | 73.0 | #3 of 32 | Archive leaderboard | report |
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