arXiv:2606.13894v1 Announce Type: cross Abstract: AdamW is a default optimizer for modern deep learning, but its first and second moment states add roughly two parameter-sized buffers to training memory. We propose Gefen, a memory-efficient optimizer that automatically shares second-moment estimates across parameter blocks and quantizes the first moment using a learned codebook, thereby reducing AdamW's memory footprint by ~8x while maintaining the same performance, corresponding to a reduction of 6.5 GiB per billion parameters. The method is motivated by a theoretical result showing that larg
The continuous scaling of deep learning models and the increasing demand for computational efficiency are driving innovation in core optimization techniques, making memory efficiency crucial.
Optimizing memory footprint for AI model training allows for the development and deployment of larger, more complex models on existing hardware, democratizing access and reducing infrastructure costs.
The ability to significantly reduce memory requirements for deep learning optimizers means that high-performance AI training can become more accessible and cost-effective.
- · AI compute infrastructure providers
- · Deep learning researchers
- · Startups building large AI models
- · Data centers and cloud providers
- · Inefficient AI training methods
- · Existing hardware constrained by memory
Reduced operational costs for training large AI models.
Acceleration in the development and deployment of more sophisticated AI applications across various industries.
Potentially lowers the barrier to entry for developing competitive AI models, leading to increased innovation and decentralization in the AI landscape.
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Read at arXiv cs.AI