The blog post examines and compares three large language model (LLM) inference frameworks—SGLang, vLLM, and TensorRT-LLM—when serving the GPT-OSS-120B model on NVIDIA H100 GPUs, highlighting their unique strengths and performance characteristics. SGLang excels in structured data generation with low latency due to its RadixAttention and specialized state management, making it suitable for applications needing consistent token generation timing. vLLM leads in throughput with efficient memory management and quantization support, making it ideal for high-concurrency applications requiring quick initial responses. TensorRT-LLM, optimized for NVIDIA GPUs, delivers the best single-request throughput but struggles with scaling, being more suitable for low-concurrency scenarios where hardware efficiency is prioritized. The post emphasizes that the choice of framework should align with specific workload requirements and hardware capabilities, as each framework is optimized for different goals and performance characteristics may vary with GPU hardware.