The author also highlights the importance of storage in improving processor acceleration and utilization. Transitioning from HDDs to SSDs can reduce latency and boost performance efficiency. However, for large-scale AI and accelerated processing to work effectively, a high-performing, scalable storage infrastructure is required. This infrastructure should be able to deliver fast write performance and feed massive amounts of data to the AI computing systems while minimizing data movement and data center real estate. The use of GPUs and DPUs in accelerated computing systems can reduce energy usage to a fraction of current levels.
Key takeaways:
- Large language models (LLMs) are evolving in AI development, improving accuracy and performance but also increasing scale and complexity.
- Industry experts suggest a transition from CPU-based to GPU-based infrastructure to support massive parallel processing and deliver accelerated computing for heavier AI workloads.
- Storage also plays a key role in improving processor acceleration and utilization, with the transition from HDDs to SSDs being a key facilitator for today's AI environments.
- Optimal compute performance and data center productivity can best be realized by organizations that deploy complementary data management infrastructure and parallel storage architectures to process data for large AI models.