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As AI adoption expands, organizations must make deliberate choices about where models are trained, tuned, and run for inference –and how those workloads are distributed across enterprise infrastructure. Hybrid AI strategies distribute AI workloads across data centers, cloud platforms, and edge environments to balance performance, scalability, governance, and cost.
- Key takeaways
- What infrastructure do AI training and inference workloads require?
- How do you build AI infrastructure that scales with workload growth?
- How do enterprises coordinate AI workloads across hybrid environments?
- How should enterprises decide where AI workloads run?
- What barriers make enterprise AI difficult to scale?
- How should enterprises compare on-premises, cloud, and edge AI deployment?
Key takeaways
- Hybrid AI enables optimized workload placement: Organizations run AI workloads across on-premises infrastructure, cloud platforms, and edge environments based on workload requirements.
- Training, fine-tuning, and inference often run in different environments: These workloads require different infrastructure capabilities and performance characteristics.
- Enterprise AI infrastructure integrates multiple components: Accelerated compute, high-speed networking, high performance storage, and orchestration platforms work together to support AI workloads.
- Workload placement decisions depend on several factors: Latency, data governance, compliance requirements, and cost predictability influence infrastructure choices.
- Integrated platforms support hybrid deployments: Solutions such as Dell AI Factory with NVIDIA combine AI infrastructure, AI software, and services into a unified solution that can be deployed across hybrid AI environments.
AI deployments are expanding across enterprise environments, and with that growth comes a new infrastructure challenge. Enterprises must determine where different AI workloads should run. Training models, managing large datasets, and delivering predictions require different computing environments. Some workloads depend on large accelerated clusters capable of processing massive datasets, while others must operate close to users or devices to respond quickly.
The scale of AI infrastructure is also increasing. The Stanford HAI 2025 AI Index Report notes that compute used to train advanced AI models continues to grow as models become more complex and datasets expand. At the same time, as AI adoption accelerates, enterprises are reaching an inference inflection point—shifting from model development to large-scale deployment of AI in production. The Databricks State of Data + AI Report found that organizations deployed 11 times more AI models into production year over year, reflecting the rapid growth in enterprise AI deployments.
As AI deployments move beyond experimentation, enterprises are adopting hybrid AI architectures that distribute workloads across data centers, cloud platforms, and edge environments. Dell AI Factory with NVIDIA supports this approach by integrating accelerated compute, networking, storage, and AI software into solutions capable of operating across hybrid environments.
What infrastructure do AI training and inference workloads require?
Large-scale AI workloads require infrastructure designed for distributed computing. Training modern machine learning models involves processing large datasets across multiple compute nodes, while reasoning-driven inference introduces new demands requiring sustained, multi-step compute to generate high quality outcomes in production.
Enterprise AI infrastructure typically includes several key components:
- GPU-accelerated compute clusters: Enable parallel processing required for large-scale AI training and inference workloads
- High-bandwidth networking: Allows compute nodes to exchange large volumes of data during distributed training
- Distributed high-performance storage systems: Store and manage large datasets used for training machine learning models
- AI frameworks: Support model development, experimentation, and training workflows
- Workload orchestration platforms: Coordinate distributed computing environments that run AI workloads
These components allow AI workloads to operate across distributed clusters instead of individual servers. During training, models repeatedly exchange parameters across compute nodes, making networking performance and storage throughput critical.
Infrastructure demands continue to grow as models become larger and datasets expand. According to NVIDIA, AI training workloads are highly resource-intensive due to complex model architectures, optimization techniques, and repeated training iterations. Even relatively small models trained on limited datasets can require significant compute, memory, and energy resources. As these requirements increase, organizations adopt integrated infrastructure platforms that simplify deployment and support large-scale AI workloads.
How do you build AI infrastructure that scales with workload growth?
AI projects often begin as experimental initiatives but eventually require infrastructure capable of supporting long thinking AI applications across multiple teams and business functions.
One approach to scaling AI infrastructure is the use of distributed GPU clusters. These clusters allow training workloads to run across multiple compute nodes simultaneously, reducing the time required to train complex models.
Hybrid infrastructure also plays a critical role in scalability. By combining on-premises data centers with cloud platforms, enterprises can maintain control over sensitive datasets while expanding compute capacity when workloads increase.
Containerized development environments help data science teams move models from development to production without rebuilding infrastructure. This consistency allows organizations to accelerate deployment cycles and manage AI workloads more efficiently.
How do enterprises coordinate AI workloads across hybrid environments?
As AI deployments expand, managing workloads across multiple infrastructure environments becomes increasingly complex. Hybrid AI environments require coordination between training pipelines, inference services, and data pipelines operating across data centers, cloud platforms, and edge systems.
AI workload orchestration platforms help organizations manage these environments. These platforms typically support several core functions:
- Workload scheduling assigns AI workloads to available compute resources
- GPU allocation distributes GPU capacity across teams and projects
- Training pipeline coordination manages distributed machine learning workflows
- Data movement management transfers datasets across compute and storage environments
Workload orchestration also enables workload portability. Models may be trained in large data center clusters and later deployed to cloud services or edge devices, depending on operational requirements.
How should enterprises decide where AI workloads run?
AI workload placement should start with the requirements of the workload, not the infrastructure environment. Training, fine-tuning, inference, RAG, and agentic AI can have different needs for latency, data locality, compliance, cost predictability, and scale. The framework below shows how common decision factors influence whether a workload fits best on-premises, in the cloud, at the edge, or across a hybrid architecture.
| Decision factor | What to evaluate | Typical deployment fit |
|---|---|---|
| Latency | How quickly the workload must respond to users, devices, applications, or operational systems | Edge or on-premises for real-time inference; cloud or data center for less time-sensitive workloads |
| Compliance and data governance | Whether data must remain in a specific location, environment, or jurisdiction | On-premises, private cloud, sovereign cloud, or controlled hybrid environments |
| Cost predictability | Whether usage is steady, bursty, or difficult to forecast | On-premises for sustained high-volume workloads; cloud for experimentation or burst capacity |
| Training | Amount of data, GPU capacity, storage throughput, and distributed compute required | On-premises or cloud GPU clusters, depending on data sensitivity, scale, and available capacity |
| Fine-tuning | Need for governed, domain-specific data and repeatable model updates | On-premises, private cloud, or controlled hybrid environments |
| Inference | Latency, throughput, data proximity, and expected query volume | Edge for real-time local response; on-premises or cloud for centralized application inference |
| RAG and knowledge assistants | Proximity to enterprise content, retrieval freshness, permissions, and security requirements | On-premises, private cloud, or hybrid environments close to governed data sources |
| Agentic AI | Tool access, identity controls, orchestration, observability, and auditability | Controlled on-premises, private cloud, or hybrid environments with strong governance |
What barriers make enterprise AI difficult to scale?
Scaling AI across enterprise environments introduces both technical and operational challenges.
Infrastructure capacity is often the first barrier. Training large models requires significant GPU capacity and high-speed networking, both of which can be costly to deploy and maintain.
The inference inflection point has arrived: agentic AI is now mainstream, with self-evolving, autonomous agents emerging across consumer, enterprise, and industry use cases. With 11x more models moving into production, enterprises are facing an order-of-magnitude increase in inference demand. That growth is further amplified by the shift to reasoning and long-thinking inference, where each request requires significantly more compute and token generation than traditional single-shot responses. Layer on always-on agents, and total token volume expands by yet another order of magnitude. The result is a step-function increase in infrastructure requirements—driving the need for AI factories purpose-built to deliver scalable, efficient inference at production scale.
Data management adds another layer of complexity. Machine learning models rely on large datasets that must be stored, processed, and accessed efficiently across systems. Preparing these datasets for training and making the data readily available to production AI agents often requires extensive data engineering work.
Operational complexity also increases as organizations deploy AI across departments. Monitoring models, managing infrastructure resources, and coordinating workloads across environments requires specialized tools and processes.
How should enterprises compare on-premises, cloud, and edge AI deployment?
After identifying workload requirements, enterprises can compare the infrastructure environments available in a hybrid AI strategy.
Deployment model | Advantages | Limitations | Typical AI workloads |
On-prem AI infrastructure | Strong data control, predictable costs, full infrastructure ownership | Higher upfront investment and operational management | Large training workloads, regulated datasets |
Cloud AI infrastructure | Elastic compute scaling, rapid experimentation, and access to GPU clusters | Variable costs and potential data transfer overhead | Model development and burst training |
Edge AI deployment | Low latency and local data processing | Limited compute capacity | Real-time inference and IoT analytics |
Several factors influence these decisions. Latency requirements may require inference workloads to run close to users or devices. Data governance policies may require certain datasets to remain within on-premises environments. Cost predictability can also influence where training workloads are deployed.
Platforms like Dell AI Factory with NVIDIA help enterprises deploy and manage AI workloads across hybrid environments while maintaining consistent infrastructure and operational control.
FAQs
Where should I deploy my AI workloads?
Deploy AI workloads where performance, data governance, compliance, cost, and scalability requirements are best met. Large training and fine-tuning workloads often fit on-premises or cloud GPU clusters, depending on data sensitivity and capacity needs. Inference workloads may run in the cloud, on-premises, or at the edge, depending on latency, data locality, and operational requirements. A hybrid AI strategy lets enterprises place each workload where it performs best while maintaining consistent orchestration, security, and operational control.
What is hybrid AI infrastructure?
Hybrid AI infrastructure combines on-premises data centers, cloud platforms, and edge environments to support AI workloads. This allows organizations to place workloads where performance, governance, and cost requirements align.
What is hybrid AP deployment?
Hybrid AI deployment refers to distributing AI workloads across multiple infrastructure environments, including on-premises data centers, cloud platforms, and edge systems. This approach allows organizations to place training and inference workloads where performance, data governance, and cost requirements are best met.
How do enterprises orchestrate AI workloads across hybrid environments?
Enterprises use orchestration platforms to coordinate distributed training pipelines, schedule workloads across clusters, and manage compute resources across hybrid infrastructure environments.
How do organizations design scalable AI infrastructure?
Organizations design scalable AI infrastructure by combining -accelerated compute, high-speed networking, distributed storage, and orchestration platforms that allow workloads to operate across hybrid environments.
Ready to move AI from experimentation to enterprise impact? Explore TechRepublic’s Enterprise Guide to Scalable AI for practical guidance on strategy, data, infrastructure, use cases, and ROI.