The client needed their LLM-powered product to survive a launch expected to increase traffic significantly. Rather than just adding a larger GPU, we rebuilt the serving layer on vLLM and Ray Serve — with continuous batching, PagedAttention memory management, bounded queues, and workload-aware autoscaling — so the platform could scale horizontally, keep GPUs efficiently utilized, and protect p95/p99 latency during spikes.
The original deployment relied on a single-replica inference server behind a basic load balancer. While this worked for early testing, it was not designed for production-scale LLM traffic.
As concurrency increased, the system began to show the common failure patterns of overloaded inference services: requests waited behind long-running generations, queues grew without a clear backpressure strategy, GPU memory became harder to utilize efficiently, and tail latency increased sharply. The biggest concern was not average latency, but unpredictable p95 and p99 behavior under load.
For an upcoming launch expected to increase traffic significantly, the client needed more than a larger GPU. They needed a serving architecture that could scale horizontally, keep GPUs efficiently utilized, protect latency during spikes, and provide a repeatable way to validate capacity before release.
We designed a horizontally scalable LLM serving platform centered on vLLM for high-throughput inference and Ray Serve for orchestration, routing, and replica management across GPU workers.
The architecture focused on four core principles:
This changed the serving layer from a single inference endpoint into a distributed system built around token flow, GPU memory, and tail-latency control.
We began by profiling the existing workload to establish a clear baseline across the metrics that actually matter for LLM serving: time to first token, inter-token latency, total tokens per second, request throughput, GPU memory utilization, queue depth, and p50/p95/p99 latency. From there, we iterated through the serving stack.
vLLM's continuous batching allowed the engine to keep the GPU active by admitting new requests while other requests were still generating tokens — avoiding the inefficiency of waiting for an entire static batch to finish before processing new work.
PagedAttention improved how KV-cache memory was managed. Instead of treating each request as a large contiguous allocation, the system managed attention cache memory in smaller blocks, which reduced memory waste and allowed the GPU to serve more concurrent sequences safely.
We also optimized for repeated prompt structure. Many production LLM applications reuse the same system prompt, instruction template, tool definitions, or few-shot examples across requests. By structuring prompts so shared content appeared first, the platform could take advantage of prefix caching and reduce redundant prefill computation.
Autoscaling was tuned around workload-aware serving signals rather than simple CPU or request-count metrics. Two requests can have very different costs depending on prompt length, output length, and active token generation, so the scaling policy paid attention to queue depth, active batch behavior, in-flight work, and token pressure instead of treating every request as equal.
Key implementation work included:
The redesigned platform gave the client a production-ready inference layer that could handle launch traffic with predictable latency and better GPU efficiency.
Instead of relying on a single server that became unstable under load, the new architecture scaled across multiple GPU-backed replicas. Continuous batching improved per-GPU throughput, PagedAttention made memory utilization more efficient, and bounded queues prevented uncontrolled latency growth during spikes.
The most important improvement was operational confidence. The team could now measure capacity, identify bottlenecks, tune serving parameters, and validate future traffic scenarios before they became production incidents.
The client left with: