Streamline Your AI Infrastructure Cost and ROI Management
Artificial intelligence (AI) is transforming entire industries with innovative breakthroughs requiring massive amounts of expensive compute infrastructure. Managing AI infrastructure workflow efficiently and maximizing spend on critical workloads is crucial for a solid return on investment (ROI).
Common Pain Points for
AI Infrastructure Investment
If you’re not actively managing your AI workloads, you’re likely spending too much. Without effective cost management, clusters are often spun up and left running racking up costs while under-provisioned resources can delay projects and deliver less-than-optimal value. When multiple user groups are accessing multiple systems, these challenges only grow.
High Upfront Costs
AI infrastructure (hardware, software, and services) typically require significant upfront investment.
Integration Costs
Integrating new AI systems with existing infrastructure and processes can be complex and costly.
Data Quality Issues
Because AI models are only as good as the data they are trained on, poor data quality means inaccurate predictions.
Talent Shortages
Many organizations do not have staff with AI expertise, making it difficult to manage AI implementation projects.
The Network is the Platform & Creates Opportunity for Better AI Infrastructure ROI
AI training workloads are highly interconnected and run in a continuous compute-synchronize-communicate loop. With workloads executing at the speed of the slowest connection, one slow connection can diminish the performance of an entire AI training workload. In fact, up to 30% of the wall clock in AI/ML training is spent simply waiting for the network to respond.
Given the significant cost of AI infrastructure, even small improvements in network performance can create real value from your AI infrastructure investment.
High-Bandwidth, Low-Latency Networks are Crucial for AI Workloads
Network latency refers to the time it takes data to travel across the network. Specifically for AI models unleashing a new wave of digital disruption, high latency creates critical bottlenecks—especially for real-time applications—which slows data processing and time-to-results.
High-Bandwidth, Low-Latency Networks Provide:
1. Synchronous distributed computing: When training AI models across multiple graphics processing units (GPUs), synchronization between nodes requires fast data transfer with minimal latency to avoid bottlenecks.
2. Large data volumes: Particularly during training, AI models process massive datasets that require high bandwidth networks to transfer data quickly between GPUs and storage systems.
3. Real-time processing: AI applications such as autonomous vehicles or live video analysis require low latency for real-time AI-inferenced responses.
4. Model complexity: As AI models become larger and more complex, the data transfer demand requirements grow, creating an even greater need for high bandwidth.
Inadequate Network Performance Leads to:
1. Slower model training data processing and time-to-value.
2. Reduced performance that negatively affects user experience.
3. Critical bottlenecks that lead to inefficient resource utilization.
Low-Network Latency Is a Must to Realize Optimal AI Infrastructure ROI
Low network latency directly impacts your AI infrastructure ROI. By enabling faster, more efficient workloads, low network latency helps you achieve increased productivity, enhanced user experience, reduced operational costs, greater competitive advantage, seamless real-time operations, and improved customer satisfaction—all of which directly contribute to a positive AI infrastructure ROI.
Reach out to Penguin Solutions today to learn how we design infrastructure to address AI infrastructure investment pain points and generate measurable ROI via low-latency, high-performance accelerated computing.
With enterprises increasingly turning to AI to scale operations, automate processes, and achieve transformative outcomes, we accelerate time-to-value with system architectures based on proven infrastructure designs that have been validated at scale in numerous production deployments.
AI infrastructure cost is driven by compute-intensive workloads, GPU/TPU requirements, high-performance storage, and ongoing energy and cooling demands. Understanding these helps optimize long-term investments.
Through workload consolidation, right-sizing resources, and leveraging hybrid or edge architectures, organizations can reduce costs and maximize ROI from AI infrastructure investments.
Cost optimization involves dynamic resource provisioning, utilizing open standards, and applying active monitoring to minimize overprovisioning and energy waste.
Track performance metrics like model training wall clock time, system uptime, resource utilization, and business KPIs linked to AI inference output to assess ROI accurately.
Talk to the Experts at Penguin Solutions
Reach out today to learn how we can help you reach your infrastructure project goals and maximize the return on your AI infrastructure investments.