TensorRT

TensorRT's core optimization is graph fusion, which combines multiple consecutive neural network layers into a single, highly optimized kernel. This eliminates redundant memory operations and kernel launch overhead.

• Vertical fusion: Merges sequential layers (e.g., convolution + bias + ReLU) into one kernel
• Horizontal fusion: Combines parallel layers with identical inputs (e.g., inception modules)
• Elimination of concatenation layers: Pre-allocates output buffers and writes directly to the destination, avoiding unnecessary transpose and copy operations

This reduces the total number of GPU kernel calls and minimizes the read/write traffic to global memory, which is the primary bottleneck in inference.

TensorRT supports reduced-precision inference to dramatically increase throughput while preserving model accuracy. The INT8 quantization workflow uses a calibration dataset to determine optimal scaling factors for each tensor.

• Post-Training Quantization (PTQ): Converts FP32 weights to INT8 without retraining, using a small representative dataset to measure activation distributions
• Quantization-Aware Training (QAT): Simulates quantization noise during training for higher accuracy at lower bit widths
• FP8 support on Hopper and Blackwell architectures leverages the Transformer Engine for native 8-bit floating-point tensor core operations

A typical INT8-optimized model achieves 2-3x throughput improvement over FP32 with less than 0.5% accuracy degradation on image classification tasks.

TensorRT handles variable input dimensions through dynamic shapes, allowing a single optimized engine to process batches of different sizes or images of varying resolutions without recompilation.

• Optimization profiles: Define minimum, optimum, and maximum shape ranges at build time; the runtime selects the best kernel for each actual input size
• Input binding reuse: Pre-allocates memory for the maximum shape and reuses buffers across inferences to avoid runtime allocation overhead
• Dynamic sequence length: Essential for transformer models processing variable-length text inputs in NLP workloads

This is critical for production serving where batch sizes fluctuate based on traffic patterns, eliminating the need to maintain multiple compiled engines.

TensorRT provides an extensible plugin API that allows developers to implement custom layers not natively supported by the SDK. Plugins are compiled as shared libraries and integrated into the optimization graph.

• IPluginV2DynamicExt: The modern interface supporting dynamic shapes and FP16/INT8 precision
• Plugin Creator pattern: Enables serialization and deserialization of custom layers for engine caching
• Common plugin library: NVIDIA ships pre-built plugins for operations like Group Normalization, ScatterND, and EfficientNMS

Custom plugins are essential for deploying novel architectures (e.g., custom attention mechanisms, non-standard activation functions) that fall outside the standard ONNX operator set.

The TensorRT builder explores a vast search space of kernel implementations (tactics) to find the fastest execution plan for the target GPU architecture. This is controlled through configurable optimization levels.

• BuilderFlag.kFP16 / kINT8: Enable reduced-precision kernel selection
• Tactic sources: The builder queries cuDNN, cuBLAS, and custom TensorRT kernels, timing each candidate on the actual hardware
• Timing cache: Stores the results of tactic timing runs to dramatically accelerate subsequent builds for the same or similar models
• Builder optimization level: Controls the aggressiveness of the search; Level 5 exhaustively explores all tactics for maximum performance at the cost of longer build times

The result is a hardware-specific, latency-optimized execution plan that outperforms generic framework runtimes by 3-7x on identical hardware.

TensorRT's runtime supports concurrent inference execution across multiple CUDA streams, maximizing GPU utilization by overlapping kernel execution with data transfer and host processing.

• IExecutionContext::enqueueV3(): Non-blocking inference submission that returns immediately, allowing the host to prepare the next batch while the GPU processes the current one
• CUDA stream management: Each execution context can be bound to a separate stream for parallel processing of independent inference requests
• Multi-context sharing: Multiple execution contexts can share the same compiled engine, each maintaining its own persistent state for weights and activations

This is the foundation for building high-throughput serving systems that achieve near-linear scaling with batch concurrency, saturating GPU tensor cores.