Bottom line: Native integration of Google’s Rapid Bucket with PyTorch via fsspec reduces GPU idle times through improved data throughput during model training.
Google has realized a native integration of Rapid Bucket—a high-performance storage based on the Colossus architecture—into PyTorch. This is intended to eliminate data loading bottlenecks during the training of large models.
Google has, led by Trinadh Kotturu (Senior Product Manager) and Martin Durant (Maintainer of fsspec at Anaconda), presented an integration solution that accelerates PyTorch workloads on Google Cloud. Rapid Bucket, built on Google’s Colossus storage architecture, is integrated directly into PyTorch via the standard fsspec interface.
The central technical problem is continuously supplying GPUs with data during training. As model sizes grow, data loading operations and checkpointing become training bottlenecks. Typical scenario: models must load and process terabytes to petabytes of data from remote storage systems. Conventional REST-based storage access cannot meet the required throughput rates and ultra-low-latency requirements, resulting in underutilized GPU resources.
Rapid Bucket addresses this requirement through bidirectional gRPC, which enables significantly higher data rates than HTTP. The storage runs in dedicated zonal buckets, thereby reducing wait time when retrieving data during training. For practitioners this means: better GPU utilization through faster data requests, without requiring changes to existing PyTorch codebases.
Source: ainews-dev.lumi-systems.io · Published 17 May 2026
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