TensorRT.
TensorRT is NVIDIA's inference optimisation library. It compiles a trained model into an engine tuned for a specific GPU, applying layer fusion, precision reduction and kernel selection to reduce latency and increase throughput.
Where TensorRT fits on a long engagement.
The speedups are substantial and come from real compilation work: fusing layers, selecting kernels for the specific GPU, and reducing precision where the model tolerates it. For high-volume inference the throughput improvement translates directly into fewer GPUs.
Reduced precision needs validation rather than assumption. FP16 is usually safe; INT8 quantisation requires calibration data and can degrade accuracy in ways that only appear on particular inputs. Measuring accuracy after optimisation on a representative set is not optional.
What an assigned team does with TensorRT.
Engines are built for a specific GPU architecture and TensorRT version, so they are not portable across heterogeneous fleets. That has to be part of the deployment design rather than discovered when a different instance type is provisioned.
Planning that properly is platform work assigned alongside devops as a service.
What we use TensorRT for.
- Throughput that reduces GPU count Optimised inference where hardware cost is the constraint.
- Precision validated after optimisation Accuracy measured post-quantisation on representative inputs.
- Engine builds matched to the fleet Architecture-specific compilation planned rather than discovered at deployment.
How TensorRT capacity is assigned.
Inference optimisation is assigned inside AI capacity, with accuracy validation required after any precision reduction.
Tell us what your roadmap needs TensorRT for.
A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.
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