From Model to Production Service
A model file on a GPU is not a production service. Production inference requires request queuing, continuous batching, KV cache management, health monitoring, automatic failover, load balancing across replicas, and graceful scaling. The inference engine handles all of this, turning a static model into a reliable, high-throughput API endpoint that applications can depend on.
vLLM
PagedAttention memory management delivers 2-4x higher throughput than naive serving. Continuous batching maximizes GPU utilization. OpenAI-compatible API. The default choice for most LLM inference workloads.
Text Generation Inference (TGI)
Hugging Face-maintained inference server with flash attention, tensor parallelism, and watermarking. Strong integration with the Hugging Face model ecosystem. Production-proven at scale across thousands of deployments.
NVIDIA Triton
Multi-framework model serving supporting ONNX, TensorRT, PyTorch, and custom backends. Ensemble pipelines for pre/post-processing. Dynamic batching and model prioritization. Best for organizations running multiple model types.
Horizontal Scaling
Multiple inference replicas behind a load balancer. Kubernetes HPA scales replicas based on GPU utilization or queue depth. Zero-downtime model updates with rolling deployments.
Inference Pipeline Architecture
Receive
API gateway and authentication
Queue
Request batching and prioritization
Infer
GPU execution with KV cache
Stream
Token-by-token response delivery
Monitor
Latency, throughput, errors
Receive
API gateway and authentication
Queue
Request batching and prioritization
Infer
GPU execution with KV cache
Stream
Token-by-token response delivery
Monitor
Latency, throughput, errors
Inference Infrastructure
Engine Selection
Each inference engine has strengths that map to different deployment scenarios. We recommend based on your model types, throughput requirements, and operational preferences.
vLLM for maximum LLM throughput. PagedAttention allocates KV cache memory in non-contiguous blocks, eliminating the memory waste that limits batch sizes in traditional serving. Continuous batching inserts new requests into running batches without waiting for the entire batch to complete. The result is 2-4x higher throughput than static batching at the same hardware. Supports tensor parallelism across multiple GPUs for large models.
TGI for Hugging Face ecosystem. If your models come from the Hugging Face Hub and your team is familiar with the transformers library, TGI provides the smoothest deployment path. Built-in support for GPTQ, AWQ, and EETQ quantization. Prometheus metrics endpoint for monitoring. Grammar and JSON schema constrained generation for structured outputs.
Triton for multi-model serving. When you need to serve LLMs alongside embedding models, classifiers, rerankers, and custom models on the same infrastructure, Triton provides a unified serving layer. Model ensembles chain multiple models in a pipeline. Instance groups control GPU allocation per model. Model versioning enables A/B testing in production.
Production Operations
Inference infrastructure requires the same operational discipline as any production service: monitoring, alerting, capacity planning, and incident response.
Health checks and failover. Liveness and readiness probes detect GPU failures, OOM conditions, and model corruption. Kubernetes automatically restarts failed pods and routes traffic to healthy replicas. Multi-node deployments survive individual node failures without service interruption.
Rolling model updates. Deploy new model versions without downtime. Canary deployments send 5% of traffic to the new model for validation before full rollout. Instant rollback if quality metrics degrade.
Request prioritization. Not all inference requests are equal. Interactive user requests get priority over batch processing jobs. Priority queues ensure latency-sensitive workloads are served first during peak demand.
Who This Is For
Inference infrastructure design is for organizations moving from prototype to production AI. If you have a model that works in a notebook but need it to serve hundreds of concurrent users with 99.9% uptime, this is the engineering layer that makes it possible.
Contact us at ben@oakenai.tech