Compute and provisioning
GPU capacity acquisition, node pools, drivers and CUDA stacks kept compatible with the serving engine.
AI Inference Guide
Infrastructure & Operations
Everything around the model that keeps it serving: the platform components, the operational discipline for shipping model changes safely, scaling under bursty load, running across regions and clouds, and composing multiple models into one product.
What inference infrastructure includes
Serving layer
The inference engine plus a gateway in front: authentication, quotas, routing, failover between backends.
Orchestration and scaling
Scheduling models onto hardware, autoscaling policies, dynamic allocation across workloads.
Versioning and registry
Which model, weights, prompt, and config are live where; the ability to answer that question is a prerequisite for safe changes.
Security
Access control on invocation and on model artifacts, protection of prompts and outputs, audit of who changed what.
Cost management
Cost-to-serve per workload and per feature; levers are model choice, hardware fit, batching, routing, and scaling policy.
InferenceOps: shipping model changes safely
The operational practice of treating model deployments with the same rigor as code deployments, adapted to artifacts whose failures are behavioral, not just crashes:
- Release pipeline: standardized deployment workflows with pre-release validation (quality regression suites, latency and token-rate checks) before traffic arrives.
- Progressive rollout: canary or blue-green releases comparing the candidate against the live version on real traffic, with automatic rollback wired to the comparison.
- Fault tolerance: retries with budgets, timeouts, circuit breakers, and load shedding, so one degraded model or provider does not cascade.
- Governance: a registry with lifecycle state, ownership, audit trails, and compliance metadata; deployment traceability from request back to model version.
- Cost hygiene: idle-GPU detection and utilization review; unused reserved capacity is the quietest way to burn budget.
Fast scaling and the cold-start problem
Inference traffic is bursty, and GPU minutes are expensive in both directions: over-provisioning wastes money, under-provisioning drops requests. Elastic scaling for LLMs is harder than classic autoscaling for two reasons:
Cold starts are long
A new replica pays three sequential bills: provisioning a GPU instance, pulling a large container image, and loading tens of gigabytes of weights from storage into GPU memory. Mitigations attack each stage: warm pools, image caching and slimming, weights on fast local storage or streamed in parallel. Scale-to-zero saves idle cost but puts the full cold start on the first request; decide per workload.
The obvious signals mislead
CPU utilization says little about a GPU-bound service, and reported GPU utilization can look high while the chip does little useful work. Scale on demand-side signals instead: queue depth, in-flight concurrency, and request rate, correlated with the batch sizes the engine actually achieves.
Distributed, multi-region, multi-cloud
- Distributed inference spans two scales: sharding one model across GPUs and nodes (tensor, pipeline, and expert parallelism, covered in serving optimization), and distributing a fleet of replicas across zones and clusters with state-aware scheduling, health checks, and failure recovery.
- Why teams go multi-region or multi-cloud: GPU scarcity and price differences between providers and regions, user latency, failover, data-residency law (GDPR-style constraints pin some data to jurisdictions), and lock-in avoidance.
- What it takes: replicating model artifacts near each serving location, global load balancing with capacity- and compliance-aware routing, automated failover, and unified observability with per-location cost attribution.
- The bill: cross-region traffic pays real latency and egress fees, and every additional cloud multiplies operational surface. Concentrate by default; distribute for a named reason.
Multi-model pipelines
Production features are rarely one model. A retrieval-augmented answer chains an embedding model, a retriever, often a reranker, a generator, and safety checks; document AI chains OCR, layout, classification, and summarization; multimodal assistants add per-modality encoders. Composing specialists beats one monolithic model when stages have different capability needs, different hardware sweet spots, and different scaling behavior.
- Topologies: sequential stages, parallel fan-out with merge, and conditional routing between them.
- Scale and place each stage independently; a small classifier does not need the generator's GPU class.
- Budget latency across the graph explicitly and keep stage interfaces stable; per-stage failure modes must be observable or diagnosis becomes guesswork.
- Distinct from pipeline parallelism (one model split into stages) and from agentic loops (dynamic, model-driven control flow); a pipeline's graph is fixed by the application.
Build versus buy, honestly costed
Self-managed inference infrastructure carries costs that rarely appear in the first estimate: GPU procurement in a scarce market, autoscaling and concurrency control built and tuned by hand, optimization features (prefix caching, disaggregation) that platforms include but DIY stacks must implement, GPU-aware observability, and a dependency treadmill of engine, framework, and driver versions that must move in lockstep. Add the application glue (pre/post-processing, connectors, per-model streaming and caching behavior) and the scarce, expensive talent to run it all. Weigh that recurring engineering cost against platform fees and the opportunity cost of what the team is not building; the right answer differs by scale, and revisit it as volume grows.