Triton Inference Server Release 24.05

The Triton Inference Server container image, release 24.05, is available on NGC and is open source on GitHub.

Contents of the Triton Inference Server container

The Triton Inference Server Docker image contains the inference server executable and related shared libraries in /opt/tritonserver.

For a complete list of what the container includes, refer to Deep Learning Frameworks Support Matrix.

The container also includes the following:

Driver Requirements

Release 24.05 is based on NVIDIA CUDA 12.4, which requires NVIDIA Driver release 545 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545).

The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, and R520 drivers, which are not forward-compatible with CUDA 12.3. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

GPU Requirements

Release 24.05 supports CUDA compute capability 6.0 and later. This corresponds to GPUs in the NVIDIA Pascal, NVIDIA Volta™, NVIDIA Turing™, NVIDIA Ampere architecture, NVIDIA Hopper™, and NVIDIA Ada Lovelace architecture families. For a list of GPUs to which this compute capability corresponds, see CUDA GPUs. For additional support details, see Deep Learning Frameworks Support Matrix.

Key Features and Enhancements

This Inference Server release includes the following key features and enhancements.

  • Added "namespace" label in metrics if the server is launched with "--model-namespacing=true". The label can now be used to distinguish metrics from two model with same name belonging to different namespace.
  • Response cachinghas been extended to top-level requests to ensemble models.
  • Improved the performance of Python HTTPClient library.
  • Model repository can now includemultiple model configuration filesfor a given model. The specific model configuration to use can be selected when launching the server with "--model-config-name" option.
  • `INTER_OP_THREAD_COUNT` and `INTRA_OP_THREAD_COUNT` parametercan now be set in `config.pbtxt` for PyTorch Backend to control thread counts in PyTorch model execution.
  • FIL backend is now included in Triton’s ARM-SBSA container image.
  • Triton’s vLLM Backend now support deployment of models with multiple LoRA adapters. See this tutorial to learn more.
  • GenAi-Perf added a new compare subcommand to enable generating visual comparisons of different profile runs.
  • GenAI-Perf can now accept an input file containing a single prompt string to populate input generation.

NVIDIA Triton Inference Server Container Versions

The following table shows what versions of Ubuntu, CUDA, Triton Inference Server, and NVIDIA TensorRT™ are supported in each of the NVIDIA containers for Triton Inference Server. For older container versions, refer to the Frameworks Support Matrix.

Container Version Triton Inference Server Ubuntu CUDA Toolkit TensorRT
24.05 2.46.0 22.04 NVIDIA CUDA 12.4.1 TensorRT
24.04 2.45.0 TensorRT 8.6.3
24.03 2.44 NVIDIA CUDA
24.02 2.43 NVIDIA CUDA 12.3.2
24.01 2.42 TensorRT
23.12 2.41
23.11 2.40 NVIDIA CUDA 12.3.0
23.10 2.39.0 NVIDIA CUDA 12.2.2
23.09 2.38.0 NVIDIA CUDA 12.2.1
23.08 2.37.0
23.07 2.36.0 NVIDIA CUDA 12.1.1
23.06 2.35.0
23.05 2.34.0 TensorRT
23.04 2.33.0 20.04 NVIDIA CUDA 12.1.0 TensorRT 8.6.1
23.03 2.32.0 TensorRT 8.5.3
23.02 2.31.0 NVIDIA CUDA 12.0.1
23.01 2.30.0 TensorRT
22.12 2.29.0 NVIDIA CUDA 11.8.0 TensorRT 8.5.1
22.11 2.28.0
22.10 2.27.0 TensorRT 8.5 EA
22.09 2.26.0
22.08 2.25.0 NVIDIA CUDA 11.7.1 TensorRT
22.07 2.24.0 NVIDIA CUDA 11.7 Update 1 Preview TensorRT 8.4.1
22.06 2.23.0 TensorRT 8.2.5
22.05 2.22.0 NVIDIA CUDA 11.7.0
22.04 2.21.0 NVIDIA CUDA 11.6.2 TensorRT and

for x86 Linux and SBSA

TensorRT 8.4.0 for JetPack/Jetson

22.03 2.20.0 NVIDIA CUDA 11.6.1 TensorRT 8.2.3 and

for x86 Linux and SBSA

TensorRT 8.4.0 for JetPack/Jetson

22.02 2.19.0 NVIDIA CUDA 11.6.0 TensorRT 8.2.3
22.01 2.18.0 TensorRT 8.2.2
21.12 2.17.0 NVIDIA CUDA 11.5.0 TensorRT
21.11 2.16.0

TensorRT for x64 Linux

TensorRT for ARM SBSA Linux
21.10 2.15.0 NVIDIA CUDA 11.4.2 with cuBLAS
21.09 2.14.0 NVIDIA CUDA 11.4.2 TensorRT 8.0.3
21.08 2.13.0 NVIDIA CUDA 11.4.1 TensorRT
21.07 2.12.0 NVIDIA CUDA 11.4.0
21.06.1 2.11.0 NVIDIA CUDA 11.3.1 TensorRT
21.05 2.10.0 NVIDIA CUDA 11.3.0
21.04 2.9.0
21.03 2.8.0 NVIDIA CUDA 11.2.1 TensorRT
21.02 2.7.0 NVIDIA CUDA 11.2.0 TensorRT
20.12 2.6.0 NVIDIA CUDA 11.1.1 TensorRT 7.2.2
20.11 2.5.0


NVIDIA CUDA 11.1.0 TensorRT 7.2.1
20.10 2.4.0
20.09 2.3.0 NVIDIA CUDA 11.0.3 TensorRT 7.1.3


20.07 1.15.0


NVIDIA CUDA 11.0.194
20.06 1.14.0


NVIDIA CUDA 11.0.167 TensorRT 7.1.2
20.03.1 1.13.0 NVIDIA CUDA 10.2.89 TensorRT 7.0.0
20.03 1.12.0






1.9.0 TensorRT 6.0.1
19.10 1.7.0 NVIDIA CUDA 10.1.243
19.09 1.6.0
19.08 1.5.0 TensorRT 5.1.5

Known Issues

  • When using Python models indecoupled mode, users need to ensure that the ResponseSender goes out of scope or is properly cleaned up before unloading the model to guarantee that the unloading process executes correctly.
  • TensorRT v10 does not support implicit batching. As a result, Triton no longer supports TensorRT models with implicit batch dimensions.
  • Since TensorRT v10 no longer supports implicit batch, Tritonserver will not be able to load existing TF-TRT models that use implicit batch. Therefore, we need to build TF-TRT models withdynamic batch support.
  • Multiple model configuration files are not supported by loading models with file override. Users still need to provide the model configuration by setting parameter "config" : "<JSON>" instead of custom configuration file "file:configs/<model-config-name>.pbtxt" : "<base64-encoded-file-content>".
  • Perf Analyzer no longer supports the --trace-file option.
  • TensorRT-LLM backend provides limited support of Triton extensions and features.
  • The TensorRT-LLM backend may core dump on server shutdown. This impacts server teardown only and will not impact inferencing.
  • The Java CAPI is known to have intermittent segfaults.
  • Some systems which implement malloc() may not release memory back to the operating system right away causing a false memory leak. This can be mitigated by using a different malloc implementation. Tcmalloc and jemalloc are installed in the Triton container and can be used by specifying the library in LD_PRELOAD. NVIDIA recommends experimenting with both tcmalloc and jemalloc to determine which one works better for your use case.
  • Auto-complete may cause an increase in server start time. To avoid a start time increase, users can provide the full model configuration and launch the server with --disable-auto-complete-config.
  • Auto-complete does not support PyTorch models due to lack of metadata in the model. It can only verify that the number of inputs and the input names matches what is specified in the model configuration. There is no model metadata about the number of outputs and datatypes. Related PyTorch bug:
  • Triton Client PIP wheels for ARM SBSA are not available from PyPI and pip will install an incorrect Jetson version of Triton Client library for Arm SBSA. The correct client wheel file can be pulled directly from the Arm SBSA SDK image and manually installed.
  • Traced models in PyTorch seem to create overflows when int8 tensor values are transformed to int32 on the GPU. Refer to pytorch/pytorch#66930 for more information.
  • Triton cannot retrieve GPU metrics with MIG-enabled GPU devices.
  • Triton metrics might not work if the host machine is running a separate DCGM agent on bare-metal or in a container.
  • When cloud storage (AWS, GCS, AZURE) is used as a model repository and a model has multiple versions, Triton creates an extra local copy of the cloud model’s folder in the temporary directory, which is deleted upon server’s shutdown.
  • Python backend support for Windows is limited and does not currently support the following features:
    • GPU tensors
    • CPU and GPU-related metrics
    • Custom execution environments
    • The model load/unload APIs