Hammerspace Unveils Reference Architecture for Large Language Model Training
Hammerspace , the company orchestrating the Next Data Cycle, today released the data architecture being used for training inference for Large Language Models (LLMs) within hyperscale environments.
- Hammerspace , the company orchestrating the Next Data Cycle, today released the data architecture being used for training inference for Large Language Models (LLMs) within hyperscale environments.
- The parallel file system architecture is critical for training AI as countless processes or nodes need to access the same data simultaneously.
- The Hammerspace architecture creates a unified, high-performance global data environment that provides concurrent and continuous execution of all phases of LLM training and inference workloads.
- “A high-performance data environment is critical to the success of initial AI model training.