TensorRT
NVIDIA high-performance deep learning inference optimizer and runtime that maximizes throughput and minimizes latency on NVIDIA GPUs.
In Depth
TensorRT is NVIDIA deep learning inference optimization SDK that transforms trained neural network models into highly optimized inference engines for deployment on NVIDIA GPU hardware. By applying a comprehensive suite of optimization techniques at the graph and kernel level, TensorRT can deliver significantly higher throughput and lower latency compared to running models directly through training frameworks.
The TensorRT optimization pipeline begins with importing a trained model from frameworks like PyTorch, TensorFlow, or ONNX. It then applies a series of transformations: layer and tensor fusion combines multiple operations into single GPU kernels to reduce memory transfers and kernel launch overhead; precision calibration enables running in reduced precision formats (FP16, INT8, FP8) with minimal accuracy loss; kernel auto-tuning selects the fastest implementation for each operation on the specific target GPU; dynamic tensor memory management minimizes GPU memory footprint; and multi-stream execution enables concurrent processing of multiple inference requests.
TensorRT-LLM is the specialized extension for large language model inference, adding optimizations specific to autoregressive text generation. These include in-flight batching that processes multiple requests at different stages of generation simultaneously, paged KV-cache management inspired by virtual memory systems, quantization support for large models including FP8 and weight-only quantization, and tensor parallelism for distributing large models across multiple GPUs. TensorRT-LLM powers NVIDIA NIM inference microservices.
For production deployments, TensorRT integrates with NVIDIA Triton Inference Server for model serving, providing features like dynamic batching, model ensemble pipelines, and multi-framework support. The optimization process can be automated within CI/CD pipelines, ensuring that model updates are consistently optimized before deployment. Understanding TensorRT capabilities is essential for teams deploying AI models on NVIDIA infrastructure and seeking to maximize performance per dollar.
Related Terms
NVIDIA NIM
NVIDIA Inference Microservices, a set of optimized containers that package AI models with TensorRT-LLM for high-performance, GPU-accelerated inference.
Inference
The process of running a trained AI model to generate predictions or outputs from new input data, as opposed to the training phase.
GPU Computing
The use of graphics processing units for general-purpose parallel computation, providing the massive throughput needed for training and running AI models.
CUDA
NVIDIA proprietary parallel computing platform and API that enables developers to use NVIDIA GPUs for general-purpose processing and AI workloads.
Latency Optimization
Techniques and engineering practices that reduce the response time of AI systems from input to output for better user experience and throughput.
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