NVIDIA TensorRT
NVIDIA's inference compiler that squeezes maximum performance out of a GPU in production.
TensorRT compiles a network, usually imported from ONNX, into an engine optimized for one specific NVIDIA GPU: layer and tensor fusion, automatic selection of the fastest kernels for the target architecture, and reduced precision in FP16, calibrated INT8 or FP8. The engine runs server-side through Triton or embedded on Jetson, and TensorRT-LLM applies the same approach to large language models with in-flight batching and a paged KV cache. It's our choice when the target is a fixed NVIDIA GPU and every millisecond of latency counts.
What NVIDIA TensorRT brings to your project.
Typical use cases: High-frame-rate video detection, high-throughput LLM serving, embedded inference on Jetson.
- 01
Layer fusion and kernels auto-tuned for the target GPU architecture.
- 02
FP16, calibrated INT8 and FP8 precision: lower latency and memory footprint.
- 03
TensorRT-LLM: in-flight batching, paged KV cache and quantization for LLM serving.
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Same toolchain from data center to Jetson: integrated with Triton, DeepStream and Torch-TensorRT.
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