TECHNOLOGY PROFILE · TECH-0288
vLLM
Inference engine and API server for supported language and multimodal models.
Core capability checked in primary documentation. This profile preserves the prepared catalog’s original descriptions. Advantages depend on the workload; comparisons are editorial, not benchmark results.
Where it has an edge
Paged KV-cache management and continuous batching target concurrent serving beyond simple one-request generation loops.
Best-fit problems
Self-hosted LLM endpoints with sustained concurrent traffic.
Tradeoffs & cautions
Actual throughput depends on model, hardware, sequence lengths and scheduling.
What to test before choosing
Replay representative concurrency and context lengths; measure p50/p95 latency, throughput, memory and error rate.
Runtime & hardware
Usually server CPU/GPU
Access & licensing
Open-source core; deployment costs separate
Execution
Local / self-hosted
Lifecycle
Not independently audited
Comparison basis
Editorial problem-fit judgement; no head-to-head benchmark performed
Connected concepts
Alternatives to investigate
SGLang · TensorRT-LLM · llama.cpp