Executive Summary
Enterprises exploring generative AI often struggle to scale workloads across fragmented compute environments. Our client, a global technology leader, faced this exact barrier: thousands of GPUs and TPUs split between on-premises and cloud, but no unified orchestration. The result was long training queues, idle hardware, and escalating infrastructure costs.
Tymon Global solved this with a hybrid cloud AI platform built on Kubernetes and intelligent GPU/TPU scheduling. The system dynamically routed jobs to the most efficient resources, applied predictive autoscaling, and unified management under a single control plane. Training cycles that once took weeks are now finished in days, costs dropped sharply, and the client’s AI team could focus on innovation instead of infrastructure. What had been a bottleneck became a competitive advantage.
Key Results:
- GPU/TPU utilization rose to ~80% (from ~40%).
- Training throughput doubled, cutting cycle times from weeks to days.
- Infrastructure costs dropped by over 70% for large-scale training.
- High scaling achieved across on-prem and cloud with zero downtime.
- AI productivity boosted, with new models prototyped 3× faster.
Introduction
Generative AI workloads demand far more compute than a single data center can provide. Training large models like LLMs requires distributed accelerators across hybrid cloud setups on-prem GPUs paired with elastic cloud GPUs/TPUs. Today, over 90% of enterprises use hybrid or multi-cloud, and nearly 50% already run generative AI workloads in the cloud, showing rapid adoption.
The challenge is orchestration, because scaling pipelines across hundreds of GPUs/TPUs complicates scheduling, framework compatibility, and I/O. Without intelligent schedulers, idle accelerators waste both CAPEX and OPEX, which analysts call “burning cash.” Model scale intensifies the issue: GPT-3 training consumed 1,024 GPUs for a month (~$12M), while GPT-4 pushed costs near $100M. Enterprises need orchestration that maximizes utilization and ensures scalability without runaway expense.
This case study examines how Tymon Global engineered a GPU/TPU auto-orchestration layer within a hybrid cloud topology, enabling efficient resource scheduling, high utilization, and accelerated model convergence.
Data Pipeline Challenges in Hybrid Cloud AI
The client had invested heavily in both on-premise GPU clusters and reserved cloud TPU instances. But their infrastructure suffered from classic orchestration pitfalls:
- Low Utilization: Monitoring showed average GPU usage at ~15%. This matched industry studies where poorly orchestrated clusters waste the majority of available compute. Idle accelerators meant wasted energy and budget.
- Hybrid Fragmentation: On-prem clusters and cloud TPUs were treated as separate silos. No unified control plane existed, making it impossible to fluidly move workloads between environments. This complexity slowed projects and frustrated teams.
- Scaling Barriers: Large-scale model training required thousands of accelerators working in parallel. The client’s existing scheduler struggled beyond a few dozen nodes, with no robust checkpointing or preemption.
- Runaway Costs: Extended training cycles and idle resources led to excessive spending. The client estimated infrastructure costs per model were 3–4x higher than budgeted, largely due to inefficient GPU allocation and overprovisioning.
In short, the client needed a smarter orchestration layer that could unify GPU and TPU clusters, dynamically allocate workloads, and deliver consistent performance in hybrid cloud environments.
Tymon Global’s Cloud-Native Solution for Scalable Model Training
Tymon Global designed and delivered an end-to-end hybrid-cloud AI platform. Our approach combined Kubernetes and AI scheduling software to abstract, pool, and manage all accelerators as one logical cluster. Key elements included:
Step 1: Hybrid Cloud Cluster Orchestration
- Deployed a Kubernetes-based orchestration layer spanning on-prem GPUs and cloud TPUs.
- Leveraged Google Anthos and custom Kubernetes operators to federate resources into one logical cluster.
- Enabled seamless job submission with the scheduler deciding placement (on-prem GPU vs. TPU pod on GCP).
- Integrated Cloud TPU v5e pods via GKE, allowing TPU jobs to run natively within the Kubernetes workflow.
Step 2: Intelligent Job Scheduling
- Developed custom scheduling policies to direct workloads:
- Smaller PyTorch runs are prioritized on on-prem GPUs.
- Large TensorFlow jobs are dispatched to cloud TPUs for maximum throughput.
- Introduced predictive autoscaling: bursting into the cloud when local GPUs were saturated.
- Eliminated idle time by ensuring every accelerator was kept busy (“air traffic control” for AI jobs).
Step 3: Containerized Microservice Architecture
- Refactored monolithic training scripts into modular containers: data preprocessing, augmentation, training, hyperparameter tuning, and checkpointing.
- Orchestrated workflows via Kubernetes Jobs and Argo Workflows align with cloud-native DevOps practices.
- Achieved pipeline parallelism: preprocessing ran on low-cost CPUs while GPUs/TPUs handled training simultaneously.
- Transitioned legacy processes into a microservices-driven workflow, improving agility, fault tolerance, and scalability.
Step 4: Unified Data Fabric
- Built a secure, high-speed data pipeline connecting the on-prem data lake with cloud storage.
- Implemented VPN + dedicated interconnects, Google Cloud Storage FUSE mounts, and caching proxies.
- Ensured training jobs could stream data across environments with minimal latency.
- Enforced encryption in transit, access controls, and compliance-ready governance for sensitive datasets.
Step 5: Automation & DevOps Enhancements
- Automated cloud provisioning with Terraform scripts for GPU/TPU instances.
- Integrated CI/CD pipelines to automatically containerize and deploy updated model code.
- Built real-time monitoring dashboards for utilization, job health, and cloud costs.
- Configured alerts to flag anomalies (e.g., stalled jobs, cloud overspend thresholds).
Step 6: Co-Creation and Knowledge Transfer
- Maintained an open architecture (Kubernetes, Docker, Terraform) for transparency and extensibility.
- Embedded Tymon Global engineers alongside the client’s IT and data science teams.
- Conducted knowledge transfer workshops, ensuring in-house teams could sustain and extend the platform.
By the end of the engagement, the client had a single, auto-orchestrated AI training engine spanning both on-prem GPUs and cloud TPUs. This unified toolset eliminated fragmentation, streamlined development, and dramatically accelerated the journey from
AI model idea → training → deployment
Client Benefits That Matter with Tymon Global
Implementing GPU/TPU auto-orchestration within a hybrid cloud delivered measurable advantages across performance, cost, and scalability:
- Accelerated Training Cycles: End-to-end training times dropped from ~14 days to ~5–6 days, a ~60% speedup. Faster iteration enabled rapid prototyping of new generative AI models and reduced time-to-market.
- Elastic Scalability: The platform now dynamically distributes workloads across on-prem GPUs and cloud TPUs, enabling on-demand access to thousands of accelerators without additional capex. This matches hyperscaler-level scale while keeping workflows intact.
- Resource Efficiency & Cost Savings: Unified scheduling eliminated idle silicon. GPU utilization increased to ~80%, and cloud spend decreased by ~30% in the first quarter. Overall, per-model training costs fell by ~70%.
- Operational Simplification: A single orchestration layer replaced fragmented scripts and platforms, placed and retried API/UI jobs. Instead of resource management, engineers created models, increasing productivity.
- Future-Readiness: The vendor-agnostic architecture supports PyTorch, TensorFlow, and next-gen accelerators like NVIDIA H200 and Graph core without re-engineering.
Competitive Differentiation: Customer’s can now receive LLM training in-house, which increases innovation, attracts top AI talent, and protects IP.
In effect, Tymon Global’s solution shifted AI infrastructure from a bottleneck into a strategic enabler, aligning technical execution with long-term innovation goals.
High-Impact Results by Tymon Global
Following deployment, the improvements were measurable and transformative. The table below highlights key performance indicators that demonstrate how GPU/TPU auto-orchestration in the hybrid cloud environment unlocked efficiency, scalability, and cost advantages at enterprise scale.
|
Metric |
Before Implementation | After Implementation |
Impact |
|
GPU Utilization |
~15% | ~80–85% | 5–6× increase in efficiency, aligned with GPU Cloud benchmarks |
|
Model Training Time |
~14 days per flagship run | ~7 days | 2× faster completion; parallel experiments enabled |
|
Training Cost |
100% baseline | ~30% of baseline | ~70% reduction in per-model training cost |
|
Scalability |
Limited cluster capacity | Multi-TPU jobs across 50,000+ TPU chips | Enterprise-scale parallelism comparable to Google’s internal benchmarks |
|
Energy Efficiency |
High idle energy waste | Optimized hardware scheduling | Significant energy reduction without performance loss |
These findings support Tymon Global’s hybrid cloud orchestration. The customer saved money and gained hyper scaler experimentation capabilities by minimizing hardware consumption, halving training cycles, and enabling unprecedented scalability. Better scheduling saves electricity, boosting sustainability. These results demonstrate how Tymon Global powers next-generation AI innovation with infrastructure.
Future Innovation Powered by Tymon Global
After installing the hybrid orchestration platform, the client can expand. Adding edge GPUs/TPUs for on-site inference, piloting real-time pipelines with serverless autoscaling, and supporting Ray and TensorFlow Extended are the next developments. The hardware-agnostic approach allows simple integration of next-gen accelerators like NVIDIA Grace or future TPU versions without workflow change.
Centralized orchestration makes AI infrastructure a competitive edge for similar organizations. AI-aware automation and unified scheduling across on-prem and cloud provide innovation-driven scalability.
Tymon Global, a renowned digital product engineering organization, specializes in cloud, AI, and hybrid computing. We can provide a performant, cost-effective, and future-ready end-to-end solution for data centers, clouds, and edge sites.
Contact Tymon Global today to architect your next-generation AI infrastructure.