Inference Engineering

LLM Serving Frameworks in Production: SGLang vs. vLLM vs. TensorRT-LLM Architecture, Memory Management, and Throughput Trade-Offs

Serving large language models in production has moved beyond simple iteration-level scheduling. While basic continuous batching solved the fundamental problem of GPU starvation during autoregressive decoding, modern production demands have shifted the engineering bottleneck toward KV cache memory reuse, compilation overhead, and structured generation efficiency. Three dominant serving runtimes currently lead high-throughput deployments: vLLM, SGLang, and NVIDIA TensorRT-LLM. Each framework appr