Accelerated Innovation

Leveraging Specialized Hardware (GPUs/TPUs)

By /

Leveraging Specialized Hardware (GPUs/TPUs)

Description

This capability focuses on how effectively an organization leverages specialized hardware-such as GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units)-to support GenAI workloads. It includes identifying optimal hardware types for specific use cases, managing performance and cost tradeoffs, and ensuring access to infrastructure that can scale with GenAI needs.

Why it's Important

GenAI models are compute-intensive. Without the right hardware, organizations face slow inference times, long training cycles, and unsustainable costs. GPUs and TPUs offer the parallel processing power necessary to run large language models, enable real-time responsiveness, and support iterative development. However, simply procuring hardware isn’t enough-teams must also optimize resource allocation, align infrastructure with use cases, and integrate specialized compute into broader AI workflows. Organizations that master this capability unlock faster time to insight, higher-quality outputs, and a stronger foundation for scalable GenAI adoption.

Why it's Challenging @ Scale

  • Fragmented Infrastructure Strategies: Different teams and business units often rely on inconsistent or siloed approaches to acquiring and managing specialized hardware
  • Limited Access to High-Performance Compute: Many organizations struggle to secure sufficient GPU/TPU resources-especially during peak demand periods
  • High Cost of Specialized Hardware: The expense of acquiring and maintaining GPUs/TPUs can limit experimentation and scale if not managed strategically
  • Skills Gap in Hardware Optimization: Few teams have deep expertise in tuning GenAI workloads to maximize efficiency on specialized hardware
  • Vendor and Platform Lock-In Risks: Overreliance on a single cloud or hardware provider can create constraints and reduce deployment flexibility

Complexity

High: Maturing this capability requires technical specialization, forward-looking infrastructure planning, and seamless alignment between hardware availability and evolving GenAI workloads

Ready to accelerate your GenAI journey?

Click below to validate your access

Let's go!

Taking Action

Though most organizations begin their GenAI journey with significant knowledge gaps, there are targeted actions that can be taken to accelerate the process. Select your group’s current maturity, based on your assessment results, and act today.

The most important part of any journey is starting… To move from “Exploring” to “Experimenting”, focus on the following key actions:
  • Explore Key Concepts & Best Practices: Complete the Building Extensible GenAI Solutions (Routers, Tools & Agents) workshop (2 hrs.) to understand foundational key concepts and explore applied best practices
  • Exploring Extensibility in GenAI Architectures.
  • Reviewing Core Router, Tool, and Agent Concepts.
  • Identifying Use Cases for Modular Expansion.
  • Aligning Extensibility to Business and Tech Goals.
  • Planning for Long-Term Maintainability.
  • Define Your Action Plan: Outline concrete, prioritized steps your organization will take to implement GenAI Strategy.
  • Align on your Current State and define your Target State.
  • Create an actionable enablement plan.
  • Define target timeline and measures of success.
  • Deliver Quick Wins: Small, high-impact GenAI projects that can demonstrate tangible value in a short time frame.
  • Provision Hardware for Pilot Use Cases: Secure short-term access to GPUs/TPUs through cloud providers or local setups for experimentation.
  • Run Performance Benchmarks Across Hardware Types: Test workloads on different accelerator types to assess tradeoffs in cost, speed, and efficiency.
  • Launch a Hardware Readiness Checklist: Create a lightweight checklist to ensure that key infrastructure requirements are in place before project kickoff.
To move from Experimentation to “Lifting-Off”, prioritize the following actions:
  • Complete one or more of our Deep Dive Courses: Begin exploring key concepts and best practices, including:
  • Tool Selection and Integration.
  • Tool Orchestration and Controls.
  • Data Handling and Security.
  • Tool Management.
  • Tool Explainability & Customization.
  • Tool Chaining.
  • Self-Tuning Tools.
  • Tool Cost Optimization.
  • Nail It Before You Scale It: Assess and optimize your solution or process before adopting it at scale
  • Assess Your Proposed Solution or Process: Evaluate whether current workloads are aligned to the right hardware types based on latency, throughput, and cost targets.
  • Define in-scope Processes and Guardrails: Document when and how to provision GPUs/TPUs for GenAI workloads, including project thresholds and approval workflows.
  • Close any Data or Measurement Gaps: Ensure you are capturing compute utilization metrics to inform future provisioning and capacity planning.
  • Define Your Adoption & Scaling Plan: Create a structured roadmap for how GenAI solutions will be rolled out across teams, workflows, or business units
  • Define Your Phased Implementation Plan: Outline a multi-stage plan to expand GPU/TPU access from pilots to enterprise-scale deployments.
  • Build Awareness and Finalize Enablers: Share hardware usage guidelines, performance benchmarks, and allocation policies across teams.
  • Operationalize Your Comms Plan: Establish channels to communicate hardware availability, scheduling, and support contacts across business units.
To move from Lifting-Off to “Accelerating”, prioritize the following actions:
  • Formalize Your Best Practices: Document and standardize what’s working to ensure consistent, scalable success across teams and use cases
  • Standardize Hardware Allocation Policies: Create clear, role-based guidelines for GPU/TPU usage across business units.
  • Publish Optimization Playbooks: Document hardware configuration, tuning strategies, and workload matching best practices.
  • Integrate Performance Monitoring into Dev Workflows: Embed compute usage tracking and alerting into existing GenAI development pipelines.
  • Accelerate Your Adoption: intensifying efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers
  • Expand Hardware Access Across Teams: Broaden GPU/TPU availability to new projects and departments based on proven demand.
  • Launch Cost Optimization Initiatives: Actively monitor and manage usage to balance speed and spend across shared infrastructure.
  • Provide On-Demand Training for Engineers: Enable technical teams to develop deeper skills in workload-to-hardware optimization.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum
  • Showcase Performance Gains from Specialized Hardware: Share before-and-after metrics to highlight the value of hardware investment.
  • Publish Internal Case Studies: Document successful projects that effectively leveraged GPU/TPU infrastructure.
  • Recognize Operational Champions: Highlight the contributions of infrastructure, DevOps, or ML engineers driving hardware success.
The “Accelerating” stage represents “Target State” for many capabilities. “Breaking Away”, on the other hand, suggests that the specific Capability represents a clear competitive advantage for your business
  • Streamline & Embed: Integrate GenAI into core workflows while eliminating friction points to make usage seamless and routine
  • Embed Smart Provisioning into Tooling: Automate selection of optimal hardware types based on model size, latency needs, and budget constraints.
  • Provide Self-Service Access to Hardware Resources: Enable teams to easily reserve or schedule GPU/TPU resources without manual intervention.
  • Integrate Hardware Utilization Metrics into Dashboards: Make real-time compute insights available to engineering, product, and finance stakeholders.
  • Leverage Automation: Using GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort
  • Automate Hardware Scaling Based on Workload Type: Dynamically allocate compute based on real-time task demands.
  • Optimize Job Placement with Intelligent Queuing: Use AI-driven scheduling to route workloads to the most cost-efficient hardware available.
  • Apply Auto-Tuning for Hardware Efficiency: Continuously adjust batch sizes, memory configs, and parallelization settings to optimize performance.
  • Evolve & Further Accelerate: continuously refining GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases
  • Benchmark Compute ROI by Use Case: Track cost-performance metrics to guide future hardware investments.
  • Extend Specialized Hardware to New Modalities: Use GPUs/TPUs to support audio, video, and multimodal GenAI use cases.
  • Lead Industry Partnerships for Next-Gen Compute: Collaborate with vendors and research labs to shape the future of AI infrastructure.

Key "Watchouts"

As you take action you’ll want to avoid:

  • Underutilizing Available Resources: Leaving high-cost GPUs/TPUs idle due to lack of scheduling, awareness, or training
  • Overengineering for Low-Impact Use Cases: Applying specialized hardware to workloads that don’t require it, leading to unnecessary spend
  • Ignoring Shared Infrastructure Challenges: Failing to manage multi-team access, quotas, or queueing can cause bottlenecks and delays
  • Treating Hardware Needs as Static: Compute requirements evolve with models and use cases-planning must stay dynamic
  • Missing the Budgetary Implications: Without active cost monitoring, hardware investments can quickly exceed planned ROI

Targeted Benefits

While Leveraging Specialized Hardware (GPUs/TPUs) can be challenging, its benefits are clear and compelling, including:

  • Faster Time to Insight: Specialized compute reduces model training and inference time
  • Improved GenAI Output Quality: Hardware acceleration supports larger models and better performance
  • Greater Experimentation Capacity: Access to GPUs/TPUs enables more iteration across teams and use cases
  • Scalable Infrastructure Readiness: Future-proofing compute stack to support enterprise-level GenAI workloads
  • Competitive Differentiation Through Performance: Superior speed and responsiveness enhance both internal and user-facing AI experiences

Looking to Move Faster, and 'Go Bigger'?

Contact us to explore additional acceleration resources or support.
Click here
Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

Ask me anything about AI concepts, best practices, Accelerated Innovation solutions, or how to get started.