Naive batching and KV cache allocation cause fragmentation and poor GPU utilization. Measure impact under mixed-length traffic, not synthetic happy paths.

Latency spikes and low throughput under realistic mixed-length traffic. Track queue depth and tail latency as first-class health signals.

Performance degradation often appears first under mixed request lengths, where naive scheduling amplifies memory waste and queuing delays. Use canary rollout data to validate capacity assumptions.

KV cache is split into manageable blocks that can be allocated and reused more efficiently. Re-benchmark after model changes before broad traffic ramp-up.

Higher concurrency and better throughput without proportional memory growth. Measure impact under mixed-length traffic, not synthetic happy paths.

More efficient cache allocation improves usable capacity, which directly affects how many simultaneous requests can be served safely. Track queue depth and tail latency as first-class health signals.

Static batching waits for full batches; continuous batching schedules work as tokens complete. Use canary rollout data to validate capacity assumptions.

Lower tail latency and better token throughput for multi-tenant traffic. Re-benchmark after model changes before broad traffic ramp-up.

Continuous admission still requires request controls. Set limits on prompt and response sizes to prevent pathological queue growth.

Many apps can switch providers by changing endpoint configuration and model identifiers. Measure impact under mixed-length traffic, not synthetic happy paths.

Teams prototype quickly and then harden observability, quotas, and routing around vLLM. Track queue depth and tail latency as first-class health signals.

API-level similarity speeds migration, but parameter support and behavior details can differ. Validate critical endpoints before cutover.

Single-node GPU serving, horizontally scaled replicas behind gateways, and sharded model pools for diverse workloads. Use canary rollout data to validate capacity assumptions.

Plan around prompt length distribution, concurrency, and response length caps. Re-benchmark after model changes before broad traffic ramp-up.

Scaling replicas without traffic-shape controls can hide bottlenecks temporarily while increasing cost and instability. Measure impact under mixed-length traffic, not synthetic happy paths.

Ultra-low-latency edge use cases and highly specialized kernels may require alternatives. Track queue depth and tail latency as first-class health signals.

Use load testing, request caps, and fallback models to protect service-level objectives. Use canary rollout data to validate capacity assumptions.

Track p95/p99 latency, timeout rate, queue depth, and fallback frequency as first-class health signals during rollout. Re-benchmark after model changes before broad traffic ramp-up.

Start with canary traffic, instrument latency and failure modes, then scale with autoscaling and circuit breakers. Measure impact under mixed-length traffic, not synthetic happy paths.

Re-run load tests for new model versions and context-window changes before broad rollout. Track queue depth and tail latency as first-class health signals.

Tie performance revalidation to model updates, tokenizer changes, and traffic shifts so serving assumptions remain current. Use canary rollout data to validate capacity assumptions.