A production AI system is not just a model endpoint. It is a set of cooperating layers: compute and storage, workload orchestration, model and cache movement, serving coordination, inference engines, and end-to-end validation. Give every layer a defined contract and owner, then choose whether to scale the whole service or individual stages such as prefill, decode, and routing. That approach lets you place workloads appropriately on workstations, data centers, clouds, or at the edge without treating one vendor’s reference design as a universal answer.
What a modular AI stack contains
Start with a map of responsibilities rather than a product list. NVIDIA’s Inference Reference Architecture separates production inference into the following concerns. The boundaries can be implemented with different products, but each responsibility still needs an explicit owner.
| Layer | Primary responsibility | Interfaces and signals to define |
|---|---|---|
| Infrastructure | Provides CPU/GPU compute, networking, storage, and locations for model artifacts and caches. | Capacity, placement constraints, network paths, storage classes, and hardware health. |
| Orchestration and scheduling | Places workloads, maintains desired state, discovers services, and handles deployment and horizontal scaling. | Declarative resources, scheduling rules, readiness/liveness signals, and replica status. |
| Model and cache movement | Moves model weights, tokenizer files, compiled artifacts, and runtime caches to the processes that need them. | Artifact identity, transfer protocol, cache validity, bandwidth limits, and completion/failure events. |
| Model-serving orchestration | Coordinates serving workers, request routing, model-specific behavior, and lifecycle dependencies. | Serving API, routing policy, dependency order, admission rules, and rollout state. |
| Inference engines | Execute the model on the selected accelerator or CPU and expose throughput and latency behavior. | Engine configuration, supported model formats, batching policy, memory use, and per-request metrics. |
| Performance validation | Measures the complete path, from request arrival through response, under representative load. | Workload definition, latency percentiles, throughput, errors, saturation, and regression thresholds. |
This decomposition prevents a common failure: asking the scheduler to make serving decisions, or asking an inference engine to own cluster lifecycle. Kubernetes can operate workload components, but it does not automatically select an inference engine or optimize every request. Those decisions belong in the serving and engine layers.
Where Kubernetes fits—and where it stops
Kubernetes supplies the cloud-native substrate for declarative APIs, controllers, scheduling, service discovery, packaging, and horizontal scaling. NVIDIA’s Inference Reference Architecture states: “Kubernetes is the primary orchestration layer for cloud-native inference workloads.” In a modular design, Kubernetes places and keeps components running while a serving layer coordinates inference-specific behavior above or alongside it.
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Use Kubernetes for platform state
- Declare deployments, jobs, services, configuration, and resource requests.
- Constrain placement by accelerator type, memory, locality, or network topology.
- Replace failed instances and expose readiness and liveness status.
- Scale replicas when a defined metric or controller policy requires it.
Keep serving policy explicit
- Route requests to the appropriate model or stage.
- Coordinate batching, admission control, and model-specific dependencies.
- Expose serving transitions such as loading, warm, draining, and ready.
- Define what happens when a downstream stage is unavailable.
The resulting interface should say which Kubernetes resource owns a process, which serving controller decides where a request goes, and which metric triggers each scale action. Without that separation, an apparently healthy pod can still represent an unavailable model or a broken request path.
Why split inference into cooperating services?
A single replicated endpoint is often sufficient for a modest deployment. Larger or more complex systems can disaggregate inference so that stages with different resource profiles are managed independently.
Prefill
Prefill processes the input context and prepares the state needed to generate output. Its workload is tied closely to prompt length and can have different compute and memory behavior from generation.
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Decode
Decode produces output tokens over time. It can require sustained, latency-sensitive execution and may scale differently from prompt processing.
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Routing
Routing selects the model or worker and passes requests and intermediate state to the appropriate stage. It may need broad service discovery and enough capacity to absorb bursts even when model workers are warm.
Separating these stages allows distinct dependencies, placement rules, and replica counts. It also introduces network transfers, lifecycle coordination, and more failure modes. Treat disaggregation as an architectural option, not a default requirement.
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NVIDIA Grove is an example of a Kubernetes API for multi-component workloads. Its project description presents declarative definitions for roles, dependencies, startup order, and scaling rules. A Grove-style design can express that routing starts only after required workers are discoverable, or that a decode pool scales independently from prefill. The same concepts can be implemented with other controllers; the important decision is to make the dependency graph and ownership visible.
Integration contracts to write before deployment
For every seam between components, document the contract before wiring software together. NVIDIA’s architecture gives this operational guidance: “Record which inference component makes each control-plane decision, which component performs each data-plane movement, which signal makes the transition observable, and which architectural rollback returns the service to the last working state.”
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- Configuration ownership: identify the single owner for model settings, batching limits, placement, and rollout parameters. State which settings may be overridden downstream.
- Identity and secrets: define how workers authenticate to registries, object storage, queues, and one another; rotate credentials without taking the service offline.
- Dependency order: describe artifact download, cache warm-up, worker readiness, router registration, and traffic admission in sequence.
- Data-plane movement: name the component that transfers weights, caches, requests, and intermediate state, along with bandwidth and retry behavior.
- Health signals: distinguish process health from model readiness and end-to-end request health. Publish transitions that operators can alert on.
- Scaling trigger: record the metric, threshold, cooldown, and maximum rate for each independently scaled component.
- Rollback method: define the version, configuration, artifacts, and routing state that restore the last known working service.
Three different meanings of “scale”
Decide which scaling problem you have; the answer determines the architecture.
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| Scaling question | Typical action | Trade-off |
|---|---|---|
| More requests for the same complete service? | Add replicas of the serving endpoint and distribute traffic. | Simpler operations, but every replica may carry the full model footprint. |
| One stage is the bottleneck? | Scale prefill, decode, routing, or another component independently. | Better resource efficiency, with more coordination and network traffic. |
| One node cannot supply capacity or memory? | Distribute workers across nodes or clusters with explicit placement and data paths. | Higher capacity, but topology, transfer latency, and failure handling become critical. |
Kubernetes scheduling and scaling address the broader platform. A serving orchestrator or a multi-component controller must add the inference-specific policies: which stage receives traffic, when a worker is considered warm, and how a dependency outage propagates.
Choose a deployment location by workload, not fashion
NVIDIA’s NIM material presents deployment across workstations, data centers, cloud environments, and edge sites. That availability does not establish a universal best location. Compare each option against the workload and operating model.
| Location | Strengths to evaluate | Questions and constraints |
|---|---|---|
| Workstation | Local development, experimentation, and inference close to the developer or operator. | GPU memory, model compatibility, thermals, software support, and limited expansion. |
| Data center | Predictable private capacity, controlled networking, and data locality. | Hardware procurement, cluster operations, power, cooling, and expansion lead time. |
| Cloud | Elastic capacity, managed infrastructure options, and geographic choice. | Accelerator availability, transfer costs, quota limits, multi-tenant isolation, and recurring spend. |
| Edge | Low user or sensor latency and reduced movement of sensitive data. | Remote operations, constrained capacity, intermittent connectivity, and fleet-wide updates. |
Use latency and user proximity, data locality and governance, peak versus sustained demand, elastic scaling, hardware availability and cost structure, and operational expertise as the comparison axes. A system may also be hybrid: develop on a workstation, serve steady demand in a data center, and place a latency-critical subset at the edge.
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- Designed to work with Windows or Mac computers, this external hard drive makes backup a snap just drag and drop
- To get set up, connect the portable hard drive to a computer for automatic recognition software required
- This USB drive provides plug and play simplicity with the included 18 inch USB 3.0 cable
- The available storage capacity may vary.
Hardware is a conditional path
A GPU workstation or accelerator server can make sense for local development or inference, but there is no universal configuration in the available NVIDIA material. Select hardware only after specifying the model, context length, concurrency, latency target, quantization or precision, and software support. Verify GPU memory and interconnect requirements against the actual engine and model; a retail listing, stock level, or price is not implied by the general workstation category.
Validate the whole path, not one component
Benchmark the integrated request path with the model, prompt distribution, concurrency, streaming mode, and placement you expect in production. Capture throughput, inter-token and end-to-end latency, queue time, errors, accelerator utilization, memory pressure, network transfer time, and recovery behavior. Repeat measurements after changing an engine, router, cache policy, or scaling rule.
NVIDIA’s NIM page publishes one vendor result for Llama 3.1 8B Instruct on one H100 SXM at 200 concurrent requests: NIM enabled reports 1,201 tokens per second and 32 ms inter-token latency, while NIM disabled reports 613 tokens per second and 37 ms inter-token latency. The publication year is not stated on the retrieved page. Treat these figures as a vendor-published result for that exact configuration, not an independent test or a guarantee for another model, accelerator, traffic pattern, or deployment.
Quick Recap
A practical build sequence
- Define the workload: name models, prompt and output distributions, concurrency, latency percentiles, availability target, data location, and budget.
- Draw the component map: place infrastructure, orchestration, artifact and cache movement, serving coordination, engines, and validation on separate boxes.
- Choose the scaling unit: begin with whole-service replicas unless measurements show a stage-specific bottleneck; introduce disaggregation when its benefit exceeds its coordination cost.
- Write contracts: document APIs, ownership, identity, dependencies, health signals, scaling triggers, and rollback for every connection.
- Deploy a thin path: run one model and one serving route, verify artifact movement and readiness, then add replicas or stages.
- Load-test representative traffic: measure the complete path and record bottlenecks by stage.
- Exercise failure and rollback: remove a worker, interrupt artifact transfer, publish an incompatible configuration, and confirm that alerts and recovery return the service to the last working state.
- Operationalize changes: version manifests, model artifacts, engine settings, and routing policy together so that a rollback is reproducible.
Questions to ask before committing to a stack
- Which component owns each control-plane decision, and can an operator identify it during an incident?
- Where do model weights, caches, requests, and intermediate states move, and what are the bandwidth and failure assumptions?
- Are prefill, decode, and routing genuinely different bottlenecks for this workload, or would disaggregation add needless complexity?
- What is the smallest independently scalable unit that improves capacity or latency?
- Which Kubernetes resources and controllers represent model readiness, draining, and rollout state?
- How are secrets, model versions, engine versions, and configuration compatibility managed?
- Which metrics trigger scaling, and do they reflect user-visible latency rather than CPU or GPU utilization alone?
- Can the team reproduce a previous deployment and roll back without manually reconstructing state?
- Does the chosen workstation, data-center, cloud, or edge location satisfy locality, governance, capacity, and operational requirements?
- What evidence will qualify the design as ready: percentile latency, throughput, error rate, recovery time, and cost under the stated workload?
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