Streamline Your AI Infrastructure Cost and ROI Management
Artificial intelligence (AI) is unleashing a new wave of digital disruption, transforming entire industries with innovative breakthroughs requiring massive amounts of expensive compute infrastructure. Managing workflow efficiently and maximizing spend on critical workloads is crucial for ROI.
Common Pain Points for
AI Infrastructure Investment
If you’re not actively managing your AI workloads, you’re likely overspending. Without proper cost management, clusters are often spun up and left running, racking up costs while under-provisioning resources can further delay projects and not deliver optimal value. These risks grow when multiple users or groups are accessing multiple systems.
High Upfront Costs
AI infrastructure (hardware, software, and cloud services) can be expensive, requiring significant upfront investment.
Integration Costs
Integrating AI systems with existing infrastructure and processes can be complex and costly.
Data Quality Issues
AI models are only as good as the data they are trained on, and poor data quality can lead to inaccurate predictions and poor performance.
Talent Shortages
Many organizations lack the necessary personnel with AI skills and expertise, making it difficult to implement and manage AI projects.
The Network is the Platform & Opportunity for Better ROI
AI training workloads are highly interconnected—executing at the speed of the slowest connection—and run in a continuous loop of compute, synchronize, and communicate. One slow connection can slow down the performance of the entire AI training workload. In fact, up to 30% of the wall clock in AI/ML training is spent waiting for the network to respond.
Given the significant cost of AI infrastructure, even small improvements in network performance are valuable.
High-Bandwidth, Low Latency Network Is Crucial for AI Workloads
Network latency is the time it takes data to travel across a network; specifically, for AI models to process data and provide results can be a critical bottleneck, especially for real-time applications.
High-bandwidth, low latency provides:
1. Synchronous distributed computing: When training models across multiple GPUs, synchronization between nodes requires fast data transfer with minimal latency to avoid bottlenecks.
2. Large data volumes: AI models, particularly during training, process massive datasets, requiring high bandwidth to transfer data quickly between GPUs and storage systems.
3. Real-time processing: For AI applications like autonomous vehicles or live video analysis, low latency is essential to ensure AI inferenced responses.
4. Model complexity: As AI models become larger and more complex, the data transfer needs increase, further emphasizing the need for high bandwidth.
Inadequate network performance leads to:
1. Slower model training times.
2. Reduced performance impacting user experience.
3. Bottlenecks leading to inefficient resource utilization.
Low Network Latency Is Required to Achieve Optimal Infrastructure ROI
Low network latency significantly impacts return on investment (ROI) by enabling faster, more efficient workloads leading to increased productivity, reduced costs, boosted competitive advantage, seamless real-time operations, and improved user and customer satisfaction.
Reach out to Penguin Solutions today to learn our approach to AI infrastructure design to address AI infrastructure investment pain points and measurable return on investment with a focus on low-latency, high performance accelerating computing.
We accelerate time-to-value by basing system architectures on a proven set of designs that have been validated at scale in numerous production deployments.
Talk to the Experts at Penguin Solutions
Reach out today and learn more how we help you reach your AI infrastructure project goals as we design, build, deploy, and manage AI and accelerated computing infrastructures at scale.
We are ready to help.