TensorRT-LLM is a platform and toolkit designed for compiling, optimizing, and serving transformer-based models on accelerated hardware. It functions as a framework that transforms machine learning models into efficient execution graphs, providing an engine to refine these models for specific hardware to maximize throughput and minimize latency during text generation.
The project distinguishes itself through advanced execution strategies that manage the entire inference pipeline. It utilizes kernel-level fusion and static graph execution to optimize mathematical operations and computational flow, while implementing paged attention memory management to handle long sequence lengths without memory fragmentation. These capabilities are integrated with in-flight request batching and custom decoding logic, which allow for the direct implementation of sampling strategies within the execution pipeline to reduce data transfer overhead.
The toolkit supports both online model serving for scalable, concurrent request handling and offline batch inference for high-volume, non-interactive processing. It provides comprehensive controls for managing attention memory and configuring decoding parameters, ensuring that hardware utilization remains efficient across diverse deployment environments.
