Production-grade serving with vLLM, SGLang, TGI, and TensorRT-LLM. Benchmarks, deployment, batching, and the optimization techniques that decide your cost-per-token.
An inference engine is the runtime that serves a trained LLM to many concurrent users efficiently. Raw HuggingFace Transformers serve one request at a time; a production engine adds continuous batching (packing many requests into one forward pass), paged KV-cache memory management (avoiding fragmentation), and prefix caching (reusing computation across requests that share a prefix). These three innovations turn a 5-10x throughput penalty into parity with dedicated serving infrastructure.
vLLM pioneered PagedAttention and has the largest ecosystem. SGLang introduced RadixAttention, a prefix-tree cache that gives it a 29% throughput edge on prefix-heavy workloads like agents and RAG. TGI (Text Generation Inference) is HuggingFace's native runtime — easiest to deploy inside the HF ecosystem but trailing on GPU utilization (68-74% vs vLLM's 85-92%). TensorRT-LLM leads on raw latency but with NVIDIA lock-in and harder deployment.
The deciding factor is workload shape, not raw speed. Multi-turn agents and RAG with shared prefixes favor SGLang. High-throughput batch jobs on 70B+ models favor vLLM. HuggingFace-locked deployments stay on TGI. Start with the benchmark comparison below, then deploy the engine that matches your traffic.
Head-to-head comparison on H100 — throughput, latency, VRAM, and when to pick each engine for production serving.
ComparisonDocker setup, tensor parallelism, GPU memory tuning, and the OpenAI-compatible API — copy-pasteable commands.
TutorialThe radix-tree prefix cache that gives SGLang its 29% throughput edge on agents and RAG.
Deep DiveThe paged KV-cache innovation that made high-throughput LLM serving possible.
Coming soonNVIDIA's inference engine — best latency, but vendor lock-in and harder deployment.
Coming soonCost-per-token across GPU generations — which card wins for your workload.
Coming soon