Hammerspace Description
Hammerspace innovatively leverages the local NVMe storage embedded within GPU servers, converting it into a high-performance, shared storage tier designed specifically for large-scale AI training and checkpointing workloads. This approach eliminates bottlenecks inherent in legacy storage systems that struggle to keep GPUs fully utilized, while significantly reducing power consumption and external storage expenses. The platform’s parallel file system architecture supports massive scalability, allowing data to be served simultaneously to thousands of GPU nodes with minimal latency. Hammerspace integrates seamlessly with existing Linux storage servers and supports hybrid cloud environments, enabling data orchestration between on-premises and cloud infrastructure. It delivers record-setting performance validated by MLPerf benchmarks, proving its efficiency for demanding machine learning workloads. Customers such as Meta and Los Alamos National Laboratory trust Hammerspace to optimize their AI data pipelines and infrastructure investments. With quick setup and intuitive management, Hammerspace helps organizations accelerate AI projects while reducing operational complexity. By transforming underutilized storage into a powerful resource, Hammerspace drives cost savings and faster innovation.
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