AI Workloads: Management and Best Practices

AI has moved out of research labs and into the business world as a strategic core capability for modern enterprises. Most enterprises aren’t building models from scratch; they’re trying to deploy, operate, and secure existing ones. The real challenge is infrastructure. Training, fine-tuning, and serving models require clusters of expensive GPUs, large data pipelines, and reliable high-performance storage and networking. Availability, security, and compliance requirements raise the stakes further. Infrastructure that can’t guarantee those qualities turns AI from a potential business game-changer into a costly liability.

An AI workload can mean different things depending on the context. For teams that train or fine-tune, it involves model code, datasets, and distributed compute jobs that may run for days. For teams building applications, it’s the runtime environment: a model such as Llama 4, a retrieval-augmented generation (RAG) database that provides context, orchestration that handles user queries, and monitoring to keep performance steady. Both rely on the same underlying foundations (i.e., data, compute, and orchestration), and both benefit from being managed as part of a unified system rather than separate experiments.

Highlights:

• AI workload means any process that involves model training, fine-tuning, batch or real-time inference, RAG, or operational pipelines tied to orchestration and resource flow.

• Types of AI workloads include training, inference, generative tasks, retrieval-augmented generation, and upstream data engineering. Mature platforms match workload types to fit-for-purpose infrastructure.

• AI workload management combines orchestration, observability, GPU governance (pooling, partitioning, quotas), and storage/networking tuned for distributed compute.

• Mirantis provides Kubernetes-native AI infrastructure and composable tooling to help enterprises efficiently run AI workloads across hybrid environments with governance and cost control (see Mirantis AI infrastructure solutions).

What Are AI Workloads?

AI workloads execute one or more machine learning tasks such as data preparation, training, fine-tuning, evaluation, and inference. Each of these stages transforms raw data into usable intelligence. Data preparation cleans and structures information, training adjusts model parameters, and fine-tuning adapts the model to a domain. Evaluation measures model accuracy, and inference applies the model in real time for immediate results or in batch mode to process multiple requests simultaneously.

AI workloads differ from conventional applications  because they are highly data-intensive, often depend on GPUs or other accelerators, and require distributed orchestration to scale. Distributed orchestration simply means spreading large jobs across many servers that must stay in sync to complete work correctly.

Modern workflows often include agents — small programs that make decisions, call tools, and manage subtasks automatically. These agentic systems add layers of dependency management, policy control, and operational complexity (see Mirantis’ primer on agentic AI frameworks).

These workloads run on heterogeneous hardware and need schedulers that can allocate multiple GPUs per job, isolate tenants, and stay within topology constraints. For example, NVIDIA’s Multi-Instance GPU (MIG) partitions a supported GPU into several isolated instances, each with dedicated compute and memory. This makes it possible to run several inference tasks side by side without interference. See the official MIG User Guide for supported hardware and Kubernetes integration details.

Types of AI Workloads and Their Use Cases

Organizations typically run multiple workload types on a unified platform, often chained into pipelines. When you evaluate a platform, check that it supports each pattern end to end (see Mirantis’s overview of AI development platforms).

Training favors throughput and parallelism: using many GPUs working together to process large datasets quickly. Inference favors responsiveness, concurrency, and predictable cost: delivering results to users with minimal delay while serving many requests. The environment should schedule GPUs efficiently, keep data paths predictable, and avoid storage or networking bottlenecks — especially under peak demand. If GPUs sit idle waiting for data, you’re paying for expensive hardware that isn’t doing useful work.

Tools for Managing AI Workloads

Managing AI workloads effectively requires orchestration, observability, GPU control, and tuned storage and networking. Together, these keep models running smoothly, avoid idle resources, and make problems visible early.

Orchestration

Schedulers decide where jobs land, how many GPUs they request, and how retries or interruptions behave. The wrong placement can double completion time or tie up resources. Hybrid environments complicate this further because data locality affects cost and performance. Standard APIs and templates help avoid unique, hand-built deployments that can’t be reproduced elsewhere.

Observability

Performance issues often start below the model layer. A spike in latency may trace to GPU memory pressure, I/O contention, or network delays. A unified observability stack ties together model behavior, resource utilization, and infrastructure health. Keep a concise set of service-level objectives (SLOs)—for example, response time or availability—for each service or tenant, and alert only on those that matter. An SLO is a measurable promise of performance; it gives teams a shared target instead of chasing every metric.

GPU Management

Multi-Instance GPU (MIG) or fractional GPUs improve concurrency for smaller models and micro-batching. Pools and quotas protect critical inference services during spikes. Over-partitioning, however, can degrade performance for large models that need contiguous memory or fast interconnects. Keep driver and firmware versions consistent across nodes so workloads perform predictably.

Storage and Networking

Accelerators like GPUs or TPUs stay productive only when data arrives fast enough. Training demands sustained throughput for checkpoints and datasets. Inference depends on predictable response time, known as latency, to keep applications responsive. Many teams monitor “p95” or “p99” latency, meaning that 95 or 99 percent of requests must complete under a given threshold. Reducing and monitoring these outliers is essential for user-facing services such as chat or voice systems. Combine fast NVMe drives for local caching with scalable object storage for larger datasets, and use networks with stable timing (low jitter) to avoid delays.

Choosing a Cloud or Platform for AI Workloads

Every cloud vendor advertises accelerators and scale. The real decision balances availability, performance per dollar, compliance, and integration with your stack. Mirantis argues that all infrastructure is becoming AI infrastructure.

GPU and Hardware Acceleration

Availability and interconnects matter as much as peak FLOPS. NVIDIA reports that its rack-scale GB200 NVL72, which connects 72 Blackwell GPUs as a single large accelerator, achieved up to 30× higher throughput than an H200 NVL8 system on the Llama 3.1 405B benchmark in MLPerf Inference v5.0 (NVIDIA blog, Apr 2025). Independent submitters such as CoreWeave report similar gains (CoreWeave, Apr 2025). Benchmark results vary, so test your own models: sequence length, batch size, and I/O shape affect performance as much as raw speed.

Hybrid and Multi-Cloud Flexibility

As AI strategies evolve, workloads move. You need a control plane that spans on-premises systems, colocation sites, and public clouds. Mirantis k0rdent AI supports bare metal, private clouds like vSphere and OpenStack, and hyperscalers like AWS, Azure, and GCP (see docs and product page). The best architecture is often a mix: several clouds plus on-prem, all managed consistently.

Compliance and Security (Tenant Isolation)

Regulated industries require provable control over data and models: policy-as-code, artifact signing, auditable promotion flows, and the ability to trace which datasets and models ran where. Hard multi-tenancy—isolating networks, credentials, and resources—protects both compliance and performance. It also lets teams track cost and reliability independently. Mirantis provides Kubernetes-native tools for secure, sovereign environments with hard multi-tenancy (see Mirantis sovereign AI cloud and AI governance guide).

Cost and Power Efficiency

List prices rarely reflect true cost. Evaluate committed-use discounts, preemptible capacity, data egress, and (for on-prem) staffing, power and cooling. The International Energy Agency projects that global data center electricity use could double to roughly 945 TWh by 2030. That makes energy strategy a board-level concern. Major operators already practice carbon- and price-aware scheduling: shifting workloads to times or regions where power is cheaper or cleaner. Google and Microsoft both document measurable benefits (Google blog; Microsoft white paper, 2023).

Integration with Kubernetes and AI Tooling

Kubernetes is now the common substrate for AI and data-intensive systems. A 2024 Portworx survey found that 54 percent of respondents run AI or ML workloads on Kubernetes. Confirm that your environment supports GPU device plugins, storage drivers, and low-latency network interfaces. For serving, standard frameworks such as vLLM simplify scaling and isolation across many endpoints.

Application Design Patterns: RAG, vLLM, and AI Factories

When people build AI applications, they tend to reuse a few proven design patterns: common ways of wiring models, data, and infrastructure together to solve recurring problems. For example, some patterns focus on safely bringing your own data into answers, others on serving many models efficiently, and others on managing the full lifecycle of AI projects across teams.

Choosing a platform that understands and supports these patterns out of the box matters because it saves you from reinventing complex plumbing every time you start a new use case. Instead of custom-building integrations, scaling logic, and security controls, your teams can plug into ready-made building blocks that follow best practices. That makes AI projects faster to deliver, easier to operate, and much more consistent and governable as you grow. Here are some AI implementation design patterns that are common today:

Core AI Application Design Patterns

Direct Prompting: Use a hosted or self-managed model directly: the app sends a prompt and gets a response, with no extra tools or retrieval.

• Typical uses: chatbots, drafting text, summarization.

• Benefits: fastest way to build.Requirements: hard to control quality, cannot reliably use fresh or proprietary data.

Prompt Orchestration and Templates: Use reusable prompt templates and simple orchestration logic to adapt prompts to different tasks and users.

• Typical uses: role-based assistants, multi-step prompts, email or report generators.

• Benefits: better consistency and reuse than ad hoc prompts.

• Requirements: still limited to what the model “knows” from training.

Retrieval-Augmented Generation (RAG): Combine a vector database and a model-serving layer so the model can “look up” context from your own documents, knowledge bases, or APIs before answering.

• Typical uses: enterprise chat over documents, policy and procedure assistants, knowledge search.

• Benefits: uses your data without full retraining, improves relevance, and can respect access controls.

• Requirements: requires thoughtful data ingestion, chunking, retrieval, and evaluation.

Tool-Using and Agentic Patterns: Give the model access to tools and APIs (search, databases, ticketing systems, internal services) and let it decide when to call them to accomplish tasks.

• Typical uses: AI copilots that can take actions, handle workflows, or update systems.

• Benefits: moves from “answering questions” to “getting things done.”

• Requirements: needs strong guardrails, auditability, and integration with existing systems.

Workflow and Pipeline Orchestration: Chain multiple models and steps together into a repeatable pipeline: ingest data, transform, analyze, generate, verify, and route results.

• Typical uses: content moderation pipelines, document intake and classification, multi-step reasoning tasks.

• Benefits: makes complex processes reliable and observable.

• Requirements: can become brittle without good monitoring and version control.

Serving and Infrastructure Patterns

vLLM and Containerized Serving: Use an optimized serving engine such as vLLM inside containers on Kubernetes to host many models efficiently.

• Typical uses: teams that need their own private model endpoints, or want to serve multiple open and proprietary models.

• Benefits: higher throughput, better GPU utilization, safer multi-tenancy.

• Requirements: you need an underlying platform that understands GPUs, autoscaling, and isolation.

Multi-Model and Multi-Provider Routing: Route each request to the best model or provider based on cost, latency, data sensitivity, or quality requirements.

• Typical uses: cost-aware routing, “golden path” models for critical tasks, fallbacks when a provider is unavailable.

• Benefits: avoids lock-in and lets you optimize for price–performance.

• Requirements: requires good benchmarking, routing logic, and policy controls.

Fine-Tuning, LoRA, and Custom Model Patterns: Start from a base model and adapt it with your own data, using fine-tuning or parameter-efficient techniques such as LoRA and adapters.

• Typical uses: highly domain-specific assistants, brand-consistent writing, specialized classification.

• Benefits: higher accuracy on your tasks, sometimes at lower inference cost.

• Requirements: demands careful data curation, evaluation, and model lifecycle management.

Platform and “AI Factory” Patterns

Evaluation, Guardrails, and Safety Loops: Embed safety checks, content filters, evaluations, and human review into the loop, not as an afterthought.

• Typical uses: regulated industries, public-facing apps, internal knowledge assistants with sensitive data.

• Benefits: reduces risk, improves trust, supports compliance requirements.

• Requirements: adds complexity unless the platform makes it easy and reusable.

AI Factories: Treat AI as a shared production system that handles ingestion, labeling, fine-tuning, evaluation, deployment, and monitoring for many teams. Ideal AI Factory architectures enable a ‘flywheel’ of continuous improvement: feeding performance data back into upstream systems that continuously improve models, applications, and hosting efficiency.

• Typical uses: organizations running dozens or hundreds of AI use cases.

• Benefits: reuses common building blocks, enforces governance, improves GPU utilization, and speeds time-to-value.

• Requirements: Requires a shared operating model that comprehends AI and data governance and security, enabling safe use of high-value and private data to improve models, and safe use and continuous improvement of models in critical-path applications. In collaboration with technology partners (including Mirantis) NVIDIA has recently released an AI Factory for Government Reference Architecture to facilitate AI buildout by US government, military, and critical infrastructures. Mirantis has released its own reference architecture for AI Factories, further detailing how these requirements can be met with Kubernetes. 

“Neoclouds” and Shared AI Platforms: Run AI Factory capabilities as a shared, governed “neocloud”: common infrastructure for data, models, and workloads that multiple customers or business units can safely share.

• Typical uses: service providers, government or sector clouds, large enterprises with many semi-autonomous teams.

• Benefits: centralizes heavy infrastructure and expertise while keeping data and policies isolated where needed.

• Requirements: needs hard multi-tenancy, observability, cost allocation, and policy controls. 

Common Challenges

Running production AI means orchestrating distributed, stateful, and expensive systems.

GPU Bottlenecks and Scalability

Large models rely on fast interconnects. When GPUs must communicate over slower networks, performance can drop sharply. Schedulers should place related jobs close together and reserve whole nodes for large training tasks when needed. For smaller inference jobs, fractional GPUs can improve utilization. See NVIDIA’s Getting Started with MIG for configuration examples.

Limited Visibility

Problems often surface in one layer but originate elsewhere. A delay in model responses might come from storage congestion or a background database task. Standardize metrics using OpenTelemetry and GPU exporters so you can trace cause to effect. Define a few clear service-level objectives — such as “95 percent of requests complete in under 150 milliseconds” — and alert on those rather than raw metrics.

Data Governance and Compliance

Track dataset provenance, enforce controls for personally identifiable information, and require approval before models move between environments. The Mirantis AI governance guide outlines methods for audit trails, red-team testing, and policy enforcement.

Cost Overruns

Costs rise when queues are long, jobs churn, or GPUs sit idle waiting for data. Measure cost per useful output, such as tokens served or validated examples trained, instead of hourly spend. Apply idle timeouts, pause low-priority servers when unused, and use preemptible capacity for tolerant workloads.

Tool Fragmentation

Many teams juggle overlapping tools for orchestration, storage, and model serving. Consolidate on a curated platform with opinionated defaults. Rehearse upgrades and compatibility tests as a routine, not a crisis.

Managing Containerized AI Workloads at Scale

• Automate model pipelines with defined stages for training, evaluation, safety checks, rollout, and rollback. Keep artifacts signed and versioned for auditability.

• Optimize GPU scheduling with node labels and topology hints so multi-GPU jobs stay within fast interconnect zones. Use fractional GPUs for small workloads to raise utilization.

• Monitor continuously, tracking latency, throughput, and outliers, and relate them to GPU and I/O metrics.

• Enforce policy-based access using role-based controls and signed images so only approved models and datasets reach production.

• Manage hybrid clusters across data centers and clouds through a unified control plane that keeps operators and policies synchronized.

Data Center Optimization Strategies

GPU and TPU Acceleration

Profile models to match their memory and bandwidth needs with available hardware. Rack-scale systems that provide larger NVLink domains can significantly improve performance. NVIDIA’s MLPerf v5.0 report shows its GB200 NVL72 achieving more than three times the per-GPU performance of an H200 NVL8 on the Llama 3.1 405B benchmark (NVIDIA blog).

Parallel and Distributed Processing

Parallelism means splitting training across multiple devices so they share the workload. Data, tensor, and pipeline parallelism each divide the task differently. For inference, micro-batching and speculative decoding let servers process more requests in less time by predicting likely tokens ahead of confirmation.

Cooling and Power

Track power usage effectiveness (PUE), a measure of how much electricity actually drives computation versus cooling and overhead. Consider liquid cooling and heat reuse where possible. The IEA projects global data-center electricity consumption could reach about 945 TWh by 2030. Energy availability and cost will increasingly shape AI capacity planning.

Data Throughput and Storage

Keep frequently accessed (“hot”) datasets on NVMe or fast object storage and pre-stage large model checkpoints close to compute nodes. GPUs stall if they wait on data transfers. For RAG workloads, cache embeddings near serving tiers and monitor compaction jobs in vector stores, which can cause latency spikes if they run during heavy usage periods.

Automated Resource Allocation

Use autoscalers and quota policies tied to service targets and budgets. Let the system reclaim idle allocations automatically and make cost impact visible to teams so they self-optimize.

AI Workloads on Kubernetes

Kubernetes provides consistent deployment and automation across environments. Many enterprises now standardize on Kubernetes-native AI infrastructure to unify operations and security.

Orchestration and Scheduling

Run training, batch, and serving on a single substrate with distinct queues and autoscaling rules. Create node pools optimized for GPU, CPU, or memory-heavy services to avoid contention. Prioritize critical inference jobs to ensure responsiveness.

GPU Operators

Install vendor operators and device plugins to expose GPU health, availability, and MIG partitions to the scheduler. Keep driver and CUDA versions synchronized across clusters. NVIDIA’s MIG documentation covers setup and management in detail.

Observability

Integrate telemetry from models, GPUs, storage, and networks into unified dashboards. Track latency, availability, and error budgets for each service and tenant. Show cost data alongside performance so trade-offs are visible.

Autoscaling

Use horizontal and vertical pod autoscalers for serving and batch jobs. For training, scale based on queue backlog or pending time rather than CPU load. Combine autoscaling with budget constraints to prevent uncontrolled bursts.

Security

Apply admission controls and policy-as-code to images, models, and datasets. Require signed artifacts and least-privilege access. Segment networks, rotate credentials, and log every promotion for auditability.

Best Practices Summary

• Unify observability with clear performance targets tied to user outcomes.

• Automate model lifecycle management with signed artifacts and controlled promotion.

• Optimize cost with elastic scaling and preemptible capacity where appropriate.

• Embed security and compliance with policy checks, scanning, and tenant isolation.

• Continuously validate models with canary releases and A/B testing.

Mirantis k0rdent AI

Mirantis k0rdent AI connects these principles — system-level management, orchestration, cost governance, compliance, and multi-cloud scale — into a cohesive platform for enterprise AI workloads.

Why Mirantis k0rdent AI is a strong fit for AI workloads:

• End-to-end pipelines (train → serve): Mirantis k0rdent AI supports RAG pipelines, fine-tuning flows, and high-throughput serving, letting teams operate from data ingestion to production endpoints on one substrate (see Mirantis k0rdent AI’s AI Platform as a Service).

• GPU as a Service: Composable GPU pools, MIG profiles, quotas, and cost tracking support both small models with high concurrency and large models needing guaranteed capacity (GPU Platform as a Service).

• Hard multi-tenancy: Network and identity isolation, policy-as-code, and signed artifact promotion let multiple business units or customers share infrastructure securely (see sovereign AI cloud and governance guidance).

• Kubernetes-native serving stacks: Integration with GPU operators, storage and network options, and serving frameworks such as vLLM on Kubernetes enables scalable, isolated endpoints.

• AI factories: k0rdent provides the components for enterprise AI factories: shared GPU and data platforms for embedding, fine-tuning, versioning, and serving under one control plane. In collaboration with technology partners (including Mirantis) NVIDIA has recently released an AI Factory for Government Reference Architecture to facilitate AI buildout by US government, military, and critical infrastructures. Mirantis has released its own reference architecture for AI Factories, further detailing how these requirements can be met with Kubernetes. 

• Neoclouds: Using the same k0rdent building blocks, operators can stand up “neoclouds”: shared, sector- or region-specific AI platforms that expose factory-style capabilities for data ingestion, fine-tuning, versioning, serving, and observability as a reusable service across agencies, business units, or customers. Neoclouds let organizations pool GPU capacity and operational expertise, enforce strong isolation and governance for each tenant, and deliver AI services with cloud-like self-service while maintaining control over location, sovereignty, and regulatory compliance.

• Adaptive operations: With MCP AdaptiveOps, Mirantis offers an operations framework that keeps agentic infrastructure coherent as ecosystems evolve (press release).

• Hybrid and multi-cloud: One control plane spanning bare metal, private clouds like vSphere and OpenStack, and hyperscaler clouds like AWS, Azure, and GCP (see Mirantis k0rdent Enterprise and docs).

• Cost and sustainability levers: Policy-driven autoscaling and carbon-aware scheduling align with proven methods from operators such as Google and Microsoft.

What this means in practice:

• Using Mirantis k0rdent AI, a global enterprise can build an AI factory where ingestion, embeddings, fine-tuning, and multi-tenant model serving share one policy and observability model—on-prem, public cloud, or both. A cloud service provider can extend the Mirantis k0rdent AI operations model to build their brand as an agile 'neocloud' with differentiated offerings for commercial AI application hosting.

• Mirantis k0rdent AI delivers a single, composable framework for abstracting clouds and infrastructure, managing Kubernetes integrations, operationalizing compute and GPUs, defining training and hosting environments from composable open-source and partner provided components (all Mirantis-validated), deploying, and lifecycle managing all of this, from 'metal to model': replacing a host of disparate tools with a single, Kubernetes-native, GitOps-friendly declarative paradigm (and friendly webUIs).

• Platform teams standardize tenancy and isolation so business units or customers can deploy safely without performance or compliance risks.

• Operators gain GPU utilization and cost visibility, while guardrails prevent noisy neighbors from degrading critical workloads.

• Compliance teams get audit trails and provenance to show which datasets and models ran where.

If you’re building or scaling AI infrastructure, Mirantis k0rdent AI provides a platform aligned to the operational realities described here. Explore the k0rdent Enterprise overview and documentation, or see Mirantis resources on AI Infrastructure-as-a-Service and Inference-as-a-Service.