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Migrating TensorRT from 10.x to 11.x on Jetson/JetPack#

This page describes platform-specific migration considerations for TensorRT on NVIDIA Jetson platforms (JetPack) when upgrading from TensorRT 10.x to 11.x. For API-level migration details, refer to the C++, Python, and trtexec migration pages.

Platform Eligibility#

Platform

TensorRT Version

Notes

Jetson Thor (JetPack 7.x)

11.x

Full upgrade path. Follow the migration steps on this page.

Jetson Orin (JetPack 6.x)

10.x or 11.x

Optional but recommended upgrade path.

Who Should Migrate#

You need to take action if your Jetson application does any of the following:

  • Uses implicit INT8 quantization (BuilderFlag::kINT8 with IInt8Calibrator, or --int8 with trtexec).

  • Relies on weakly typed builder flags (BuilderFlag::kFP16, BuilderFlag::kBF16, BuilderFlag::kFP8, or equivalent flags such as --fp16 with trtexec).

  • Uses IPluginV2DynamicExt or IPluginCreator.

  • References removed tactic sources (kCUBLAS, kCUBLAS_LT, kCUDNN).

If your application already uses strong typing, explicit quantization (Q/DQ nodes), and IPluginV3, the upgrade to TensorRT 11.x should require minimal code changes beyond recompilation.

Migration Checklist#

  1. Migrate from weak typing to strong typing. TensorRT 11.x removes all precision-enabling builder flags. Use ModelOpt AutoCast to convert ONNX models to mixed precision before building. Refer to the Strongly Typed Networks section for details.

  2. Migrate from implicit INT8 to explicit quantization. The IInt8Calibrator class and all subclasses have been removed. Use ModelOpt Quantization to produce models with Q/DQ nodes. Refer to the C++ or Python migration page for before/after examples.

  3. Migrate plugins from V2 to V3. IPluginV2DynamicExt and IPluginCreator have been deprecated. All plugins must use IPluginV3 with IPluginCreatorV3One. Refer to the C++ plugin migration section for a complete NonZero plugin example.

  4. Update V2 API calls to V2 replacements. Several methods (getDeviceMemorySize, setWeightStreamingBudget, setDeviceMemory) have been replaced with V2 versions that use int64_t or accept additional parameters. Refer to the V2 API replacements table.

  5. Rebuild all engines. Engines built with TensorRT 10.x using the BuilderFlag::kVERSION_COMPATIBLE setting are forward-compatible with the TensorRT 11.x runtime. However, to take advantage of 11.x optimizations and new features, rebuild engines with the 11.x builder.

Version Compatibility on Jetson#

TensorRT 11.x maintains forward compatibility with TensorRT 10.x engines:

  • Engines built with TensorRT 10.x can run on the TensorRT 11.x runtime.

  • Engines built with TensorRT 8.x or 9.x are not compatible with the TensorRT 11.x runtime. TensorRT 8.x uses CUDA 11.x, which is not supported by TensorRT 11.x.

  • Backward compatibility is maintained within a major release. For example, a version-compatible engine built with TensorRT 11.5 runs on TensorRT 11.5 or any later 11.x release, but not on TensorRT 11.0 through 11.4, and not on TensorRT 10.x.

If your Jetson deployment currently uses TensorRT 8.x, you must first migrate to TensorRT 10.x using the 8.x to 10.x appendix, then follow this guide for the 10.x to 11.x migration.

CUDA and JetPack Dependency Changes#

TensorRT 11.x requires CUDA 13.2 Update 1 or later. Verify that your JetPack version includes a compatible CUDA toolkit before upgrading. Refer to the JetPack release notes for supported CUDA versions.

TensorRT 11.x drops support for CUDA 11.x. Engines built against CUDA 11.x cannot run on the TensorRT 11.x runtime.

DLA Considerations#

DLA is not supported in TensorRT 11.0; DLA support will be re-introduced in a later minor version update.

Cross-Compilation#

If you cross-compile TensorRT applications for Jetson on an x86 host, ensure that both the host TensorRT SDK and the target JetPack installations are upgraded to 11.x. Mixing TensorRT 10.x headers on the host with 11.x libraries on the target (or vice versa) will cause compilation or linker errors due to removed APIs.

Recommendation for Edge Deployments#

Tip

For Jetson applications in production, NVIDIA recommends testing the TensorRT 11.x migration in a staging environment before deploying to production devices. Verify model accuracy, inference latency, and memory usage against your TensorRT 10.x baseline.

Customers still using TensorRT 10.x should begin migrating to strong typing, explicit quantization, and IPluginV3 to reduce the scope of changes when upgrading. The TensorRT team provides mitigation plans for all removed APIs.