TensorRT-LLM2 articles

TensorRT-LLM

Articles

LLM Inference Serving Engines in Production: Comparing vLLM, SGLang, TensorRT-LLM, and TGI Architecture, KV Cache Management, Chunked Prefill, and Serving Economics
Serving large language models in production environments requires managing two fundamentally different compute regimes: the compute-bound prefill phase and the memory-bandwidth-bound autoregressive decode phase. Over the past three years, the infrastructure stack for LLM inference has shifted from naive batched execution to specialized serving engines capable of continuous batching, non-contiguous key-value (KV) cache memory management, prefix caching, and compiler-level kernel fusion. Four sys
August 28, 20261 min
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
August 18, 20261 min