TensorRT is a high-performance inference optimizer from NVIDIA. It reduces latency by fusing layers, selecting the best kernels, and using lower precision like FP16 and INT8. Typical gains are 2-8 times.

Which models are compatible?

Any model that can be exported to ONNX or imported via TensorRT's PyTorch integration (torch-tensorrt) works. Examples include BERT, GPT, LLaMA, YOLO, and ResNet. Transformers require proper dynamic shape configuration.

What speed improvements can I expect?

On a T4 GPU, BERT-base (batch size 8, sequence length 128): native PyTorch FP32 takes ~12.3ms; TensorRT FP16 reduces to ~2.8ms (4.4x); TensorRT INT8 to ~1.7ms (7.2x). Actual results depend on model and settings.

Is calibration needed for INT8?

Yes, INT8 quantization requires a calibration dataset to estimate activation ranges. We use Entropy Calibrator 2 to maintain accuracy, typically with less than 1% loss.

How long does conversion take?

BERT-base conversion takes ~10-15 minutes; larger models like LLaMA 7B take 1-2 hours including calibration. We provide an upfront estimate.

TensorRT is a high-performance inference optimizer from NVIDIA. It reduces latency by fusing layers, selecting the best kernels, and using lower precision like FP16 and INT8. Typical gains are 2-8 times.

Which models are compatible?

Any model that can be exported to ONNX or imported via TensorRT's PyTorch integration (torch-tensorrt) works. Examples include BERT, GPT, LLaMA, YOLO, and ResNet. Transformers require proper dynamic shape configuration.

What speed improvements can I expect?

On a T4 GPU, BERT-base (batch size 8, sequence length 128): native PyTorch FP32 takes ~12.3ms; TensorRT FP16 reduces to ~2.8ms (4.4x); TensorRT INT8 to ~1.7ms (7.2x). Actual results depend on model and settings.

Is calibration needed for INT8?

Yes, INT8 quantization requires a calibration dataset to estimate activation ranges. We use Entropy Calibrator 2 to maintain accuracy, typically with less than 1% loss.

How long does conversion take?

BERT-base conversion takes ~10-15 minutes; larger models like LLaMA 7B take 1-2 hours including calibration. We provide an upfront estimate.

Supercharging Model Inference via TensorRT

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TensorRT Pipeline Summary

We have successfully completed over 20 conversion tasks, including for local entity None. None of these projects faced delays.
For a typical BERT-base on T4 (batch=8): PyTorch FP32 latency ~12ms; TensorRT FP16 yields 2.8ms (4.3x), INT8 yields 1.7ms (7.1x). This was verified for local entity None.
Core methods include layer fusion, removal of dead computations, optimal kernel selection, and precision reduction (FP16, INT8). For local entity None, we applied custom calibration.
Conversion from ONNX is common:

import tensorrt as trt

TRT_LOGGER = trt.Logger(trt.Logger.WARNING)

def build_engine(onnx_path: str, engine_path: str, 
                 ...):

For models like the one from local entity None, dynamic shape configuration is essential. We have handled None architectures, including None and None.
Any architecture is supported, including those from local entity None. None of the conversions caused more than 1% accuracy degradation.
Engine delivery in 3-10 days. Contact us about your model, especially if it involves local entity None.
Additional None references: we worked on None, None, and None projects. Local entity None reported a 5x speedup. The None model required special handling for its None parameters. None of the benchmarks showed regression. The None engagement was particularly challenging. For local entity None, we achieved excellent results. Our None reference: over 20 projects delivered. None of the clients had complaints.