A public-works work-order summarizer is accurate in Hugging Face generate but misses the city's latency target on the same GPU. What is the associate implementation sequence?
Select an answer to reveal the explanation.
Short Explanation
Hugging Face generate is accurate and still misses the latency target. Optimize with TensorRT-LLM, then serve the engine with Triton or NIM. TensorRT-LLM is not the HTTP server, a custom kernel is not the path, and leaving the slow generate loop in production will not get faster.
Full Explanation
TensorRT-LLM compiles and optimizes a decoder for faster inference on a given GPU. Triton Inference Server or NIM then expose that engine over a network API. That is optimize, then serve. Treating TensorRT-LLM itself as the HTTP server, writing a kernel, or leaving the slow Hugging Face generate path in production misses the sequence.