AI Infrastructure Market Growth
– The AI infrastructure market is experiencing rapid growth, with an estimated increase from $23.5 billion in 2021 to $309.4 billion by 2031, at a CAGR of 29.8% from 2022 to 2031
– Enterprises are recognizing the potential of AI to improve operational efficiency, productivity, revenue, and cost reduction through automated and orchestrated AI/ML workflows
Scalable AI Infrastructure
– Scalable AI infrastructure is essential for businesses to handle increasing computational demands and AI workloads
– ClearML has announced enhanced orchestration and scheduling capabilities, GPU Partitioning, and MiG capabilities to drive GPU maximal utilization[1].
– FuriosaAI offers first-gen NPU WARBOY and next-gen LLM-optimized NPU with HBM3 for distributed inference
96% of companies plan to expand their AI compute capacity and investment with availability, cost, and infrastructure challenges weighing on their minds
AII Compute Capacity Expansion
– A significant 96% of companies plan to expand their AI compute capacity, with 40% considering more on-premise solutions and 60% looking towards cloud solutions
– The top concerns for cloud compute are wastage and idle costs
AI Infrastructure Challenges
– The primary challenges in scaling AI are compute limitations (availability and cost) and infrastructure issues
– 74% of companies are dissatisfied with their current job scheduling tools and face resource allocation constraints
Inference and Model Training
– Inference, using trained ML models for real-time predictions, is a critical part of moving AI into production
– Over half of the respondents plan to use LLMs in their commercial deployments in 2024
GPU Utilization and Optimization
– Optimizing GPU utilization is a major concern, with most GPUs underutilized during peak times
– Companies are planning to use orchestration and scheduling technology to maximize their existing compute infrastructure
AI Team Productivity
– 93% of respondents believe that AI team productivity would substantially increase if real-time compute resources could be self-served easily by anyone who needed it
Cost-Effective Alternatives to GPUs
– Approximately 52% of respondents are actively looking for cost-effective alternatives to GPUs for inference
– There is a lack of awareness among 20% of respondents about existing cost-effective alternatives to GPUs[1].
Compute Challenges
– The biggest challenges for compute were latency, followed by access to compute and power consumption
– Companies are employing various methods to maximize GPU utilization, including queue management, job scheduling, and multi-instance GPUs
Monitoring Compute
– For monitoring GPU cluster utilization, GCP-GPU utilization metrics and NVIDIA AI Enterprise are the most used tools
Key Drivers for AI Infrastructure Expansion
– Flexibility and speed are the top drivers for expanding AI infrastructure, with companies prioritizing these over security and budget
This comprehensive overview of the state of AI infrastructure at scale in 2024 highlights the critical role of GPUs and the challenges and considerations companies face as they scale their AI initiatives.