LLM Inference Engines in Production: Comparing vLLM, SGLang, TensorRT-LLM, and TGI
Production deployment of large language models requires balancing competing operational constraints: time-to-first-token (TTFT), inter-token latency (ITL), aggregate throughput, and VRAM utilization. Standard deep learning serving frameworks fail on autoregressive transformer inference because LLM workloads exhibit two distinct operational phases: the compute-bound prefill phase (processing the input prompt) and the memory-bandwidth-bound decode phase (generating tokens autoregressively one by o
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