The Triton Inference Server provides a cloud inferencing solution optimized for both CPUs and GPUs. The server provides an inference service via an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. For edge deployments, Triton Server is also available as a shared library with an API that allows the full functionality of the server to be included directly in an application.
New Features and ImprovementsThere was no python wheels packages released as part of 25.07 release
TensorRT calibration cache may require size adjustment in some cases, which was observed for the IGX platform.
The core Python binding may incur an additional D2H and H2D copy if the backend and frontend both specify device memory to be used for response tensors.
A segmentation fault related to DCGM and NSCQ may be encountered during server shutdown on NVSwitch systems. A possible workaround for this issue is to disable the collection of GPU metrics tritonserver --allow-gpu-metrics false ...
vLLM backend currently does not take advantage of the vLLM v0.6 performance improvement when metrics are enabled.
When using TensorRT models, if auto-complete configuration is disabled and is_non_linear_format_io:true for reformat-free tensors is not provided in the model configuration, the model may not load successfully.
When using Python models in decoupled 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.
Restart support was temporarily removed for Python models.
Triton Inference Server with vLLM backend currently does not support running vLLM models with tensor parallelism sizes greater than 1 and the default "distributed_executor_backend" setting when using explicit model control mode. In attempt to load a vllm model (tp > 1) in explicit mode, users could potentially see failure at initialize step: could not acquire lock for <_io.BufferedWriter name='<stdout>'> at interpreter shutdown, possibly due to daemon threads. For the default model control mode, after server shutdown, vllm related sub-processes are not killed. Related vllm issue: vllm-project/vllm#6766 . Please specify "distributed_executor_backend":"ray" in the model.json when deploying vllm models with tensor parallelism > 1.
When loading models with file override, multiple model configuration files are not supported. Users must provide the model configuration by setting parameter "config" : "<JSON>" instead of custom configuration file in the following format: "file:configs/<model-config-name>.pbtxt" : "<base64-encoded-file-content>".
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: https://github.com/pytorch/pytorch/issues/38273
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:
Ubuntu 24.04 builds of the client libraries and examples are included in this release in the attached v2.59.1_ubuntu2404.clients.tar.gz file. The SDK is also available for as an Ubuntu 24.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.
Jetson iGPU Support[!NOTE]
There is no Windows release for 25.07, the latest release is 25.01.
A release of Triton for IGX is provided in the attached tar file: tritonserver2.59.1-igpu.tar.
10.11.0.33, Onnx Runtime 1.22.0, PyTorch 2.8.0a0+5228986c39.nv25.6, Python 3.12 and as well as ensembles.The tar file contains the Triton server executable and shared libraries and also the C++ and Python client libraries and examples. For more information on how to install and use Triton on JetPack refer to jetson.md.
The wheel for the Python client library is present in the tar file and can be installed by running the following command:
python3 -m pip install --upgrade clients/python/tritonclient-2.59.0-py3-none-manylinux2014_aarch64.whl[all]
The Triton TensorRT-LLM container is built from the 25.04 image nvcr.io/nvidia/tritonserver:25.04-py3-min. Please refer to the support matrix and compatibility.md for all dependency versions related to 25.04. However, the packages listed below have different versions than those specified in the support matrix.
The Triton Inference Server provides a cloud inferencing solution optimized for both CPUs and GPUs. The server provides an inference service via an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. For edge deployments, Triton Server is also available as a shared library with an API that allows the full functionality of the server to be included directly in an application.
New Features and ImprovementsTensorRT calibration cache may require size adjustment in some cases, which was observed for the IGX platform.
The core Python binding may incur an additional D2H and H2D copy if the backend and frontend both specify device memory to be used for response tensors.
A segmentation fault related to DCGM and NSCQ may be encountered during server shutdown on NVSwitch systems. A possible workaround for this issue is to disable the collection of GPU metrics tritonserver --allow-gpu-metrics false ...
vLLM backend currently does not take advantage of the vLLM v0.6 performance improvement when metrics are enabled.
When using TensorRT models, if auto-complete configuration is disabled and is_non_linear_format_io:true for reformat-free tensors is not provided in the model configuration, the model may not load successfully.
When using Python models in decoupled 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.
Restart support was temporarily removed for Python models.
Triton Inference Server with vLLM backend currently does not support running vLLM models with tensor parallelism sizes greater than 1 and the default "distributed_executor_backend" setting when using explicit model control mode. In attempt to load a vllm model (tp > 1) in explicit mode, users could potentially see failure at initialize step: could not acquire lock for <_io.BufferedWriter name='<stdout>'> at interpreter shutdown, possibly due to daemon threads. For the default model control mode, after server shutdown, vllm related sub-processes are not killed. Related vllm issue: vllm-project/vllm#6766 . Please specify "distributed_executor_backend":"ray" in the model.json when deploying vllm models with tensor parallelism > 1.
When loading models with file override, multiple model configuration files are not supported. Users must provide the model configuration by setting parameter "config" : "<JSON>" instead of custom configuration file in the following format: "file:configs/<model-config-name>.pbtxt" : "<base64-encoded-file-content>".
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: https://github.com/pytorch/pytorch/issues/38273
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:
Ubuntu 24.04 builds of the client libraries and examples are included in this release in the attached v2.59.0_ubuntu2404.clients.tar.gz file. The SDK is also available for as an Ubuntu 24.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.
Jetson iGPU Support[!NOTE]
There is no Windows release for 25.06, the latest release is 25.01.
A release of Triton for IGX is provided in the attached tar file: tritonserver2.59.0-igpu.tar.
10.11.0.33, Onnx Runtime 1.22.0, PyTorch 2.8.0a0+5228986c39.nv25.6, Python 3.12 and as well as ensembles.The tar file contains the Triton server executable and shared libraries and also the C++ and Python client libraries and examples. For more information on how to install and use Triton on JetPack refer to jetson.md.
The wheel for the Python client library is present in the tar file and can be installed by running the following command:
python3 -m pip install --upgrade clients/python/tritonclient-2.59.0-py3-none-manylinux2014_aarch64.whl[all]
The Triton TensorRT-LLM container is built from the 25.04 image nvcr.io/nvidia/tritonserver:25.04-py3-min. Please refer to the support matrix and compatibility.md for all dependency versions related to 25.04. However, the packages listed below have different versions than those specified in the support matrix.
The Triton Inference Server provides a cloud inferencing solution optimized for both CPUs and GPUs. The server provides an inference service via an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. For edge deployments, Triton Server is also available as a shared library with an API that allows the full functionality of the server to be included directly in an application.
New Features and ImprovementsvLLM backend for 25.05 might be unstable with the vLLM V1 architecture. We recommend switching to V0 for this release, by setting VLLM_USE_V1 environment variable to 0. However, users should be aware that vLLM's V0 API is affected by vulnerabilities.
vLLM containers include vllm version 0.8.4 which is affected by vulnerabilities.
Workarounds:
Prior to the fix, your options include:
The core Python binding may incur an additional D2H and H2D copy if the backend and frontend both specify device memory to be used for response tensors.
A segmentation fault related to DCGM and NSCQ may be encountered during server shutdown on NVSwitch systems. A possible workaround for this issue is to disable the collection of GPU metrics tritonserver --allow-gpu-metrics false ...
vLLM backend currently does not take advantage of the vLLM v0.6 performance improvement when metrics are enabled.
When using TensorRT models, if auto-complete configuration is disabled and is_non_linear_format_io:true for reformat-free tensors is not provided in the model configuration, the model may not load successfully.
When using Python models in decoupled 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.
Restart support was temporarily removed for Python models.
Triton Inference Server with vLLM backend currently does not support running vLLM models with tensor parallelism sizes greater than 1 and the default "distributed_executor_backend" setting when using explicit model control mode. In attempt to load a vllm model (tp > 1) in explicit mode, users could potentially see failure at initialize step: could not acquire lock for <_io.BufferedWriter name='<stdout>'> at interpreter shutdown, possibly due to daemon threads. For the default model control mode, after server shutdown, vllm related sub-processes are not killed. Related vllm issue: vllm-project/vllm#6766 . Please specify "distributed_executor_backend":"ray" in the model.json when deploying vllm models with tensor parallelism > 1.
When loading models with file override, multiple model configuration files are not supported. Users must provide the model configuration by setting parameter "config" : "<JSON>" instead of custom configuration file in the following format: "file:configs/<model-config-name>.pbtxt" : "<base64-encoded-file-content>".
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: https://github.com/pytorch/pytorch/issues/38273
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:
Ubuntu 24.04 builds of the client libraries and examples are included in this release in the attached v2.58.0_ubuntu2404.clients.tar.gz file. The SDK is also available for as an Ubuntu 24.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.
Jetson iGPU Support[!NOTE]
There is no Windows release for 25.05, the latest release is 25.01.
A release of Triton for IGX is provided in the attached tar file: tritonserver2.58.0-igpu.tar.
10.10.0.31, Onnx Runtime 1.22.0, PyTorch 2.8.0a0+5228986c39.nv25.5, Python 3.12 and as well as ensembles.The tar file contains the Triton server executable and shared libraries and also the C++ and Python client libraries and examples. For more information on how to install and use Triton on JetPack refer to jetson.md.
The wheel for the Python client library is present in the tar file and can be installed by running the following command:
python3 -m pip install --upgrade clients/python/tritonclient-2.58.0-py3-none-manylinux2014_aarch64.whl[all]
The Triton TensorRT-LLM container is built from the 25.03 image nvcr.io/nvidia/tritonserver:25.03-py3-min. Please refer to the support matrix and compatibility.md for all dependency versions related to 25.03. However, the packa...
The Triton Inference Server provides a cloud inferencing solution optimized for both CPUs and GPUs. The server provides an inference service via an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. For edge deployments, Triton Server is also available as a shared library with an API that allows the full functionality of the server to be included directly in an application.
New Features and ImprovementsvLLM backend for 25.04 might be unstable with the vLLM V1 architecture. We recommend switching to V0 for this release, by setting VLLM_USE_V1 environment variable to 0. However, users should be aware that vLLM's V0 API is affected by vulnerabilities.
vLLM containers include vllm version 0.8.1 which is affected by new vulnerabilities.
Workarounds:
Prior to the fix, your options include:
The core Python binding may incur an additional D2H and H2D copy if the backend and frontend both specify device memory to be used for response tensors.
A segmentation fault related to DCGM and NSCQ may be encountered during server shutdown on NVSwitch systems. A possible workaround for this issue is to disable the collection of GPU metrics tritonserver --allow-gpu-metrics false ...
vLLM backend currently does not take advantage of the vLLM v0.6 performance improvement when metrics are enabled.
When using TensorRT models, if auto-complete configuration is disabled and is_non_linear_format_io:true for reformat-free tensors is not provided in the model configuration, the model may not load successfully.
When using Python models in decoupled 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.
Restart support was temporarily removed for Python models.
Triton Inference Server with vLLM backend currently does not support running vLLM models with tensor parallelism sizes greater than 1 and the default "distributed_executor_backend" setting when using explicit model control mode. In attempt to load a vllm model (tp > 1) in explicit mode, users could potentially see failure at initialize step: could not acquire lock for <_io.BufferedWriter name='<stdout>'> at interpreter shutdown, possibly due to daemon threads. For the default model control mode, after server shutdown, vllm related sub-processes are not killed. Related vllm issue: vllm-project/vllm#6766 . Please specify "distributed_executor_backend":"ray" in the model.json when deploying vllm models with tensor parallelism > 1.
When loading models with file override, multiple model configuration files are not supported. Users must provide the model configuration by setting parameter "config" : "<JSON>" instead of custom configuration file in the following format: "file:configs/<model-config-name>.pbtxt" : "<base64-encoded-file-content>".
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: https://github.com/pytorch/pytorch/issues/38273
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:
Ubuntu 24.04 builds of the client libraries and examples are included in this release in the attached v2.57.0_ubuntu2404.clients.tar.gz file. The SDK is also available for as an Ubuntu 24.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.
Jetson iGPU Support[!NOTE]
There is no Windows release for 25.04, the latest release is 25.01.
A release of Triton for IGX is provided in the attached tar file: tritonserver2.57.0-igpu.tar.
10.9.0.34, Onnx Runtime 1.21.0, PyTorch 2.7.0a0+79aa17489c.nv25.4, Python 3.12 and as well as ensembles.The tar file contains the Triton server executable and shared libraries and also the C++ and Python client libraries and examples. For more information on how to install and use Triton on JetPack refer to jetson.md.
The wheel for the Python client library is present in the tar file and can be installed by running the following command:
python3 -m pip install --upgrade clients/python/tritonclient-2.57.0-py3-none-manylinux2014_aarch64.whl[all]
The Triton TensorRT-LLM container is built from the 25.03 image nvcr.io/nvidia/tritonserver:25.03-py3-min. Please refer to the support matrix and compatibility.md for all dependency versions related to 25.03. However, the packages listed below have different versions...
The Triton Inference Server provides a cloud inferencing solution optimized for both CPUs and GPUs. The server provides an inference service via an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. For edge deployments, Triton Server is also available as a shared library with an API that allows the full functionality of the server to be included directly in an application.
New Features and Improvements/opt/tritonserver/backends/ directory.The core Python binding may incur an additional D2H and H2D copy if the backend and frontend both specify device memory to be used for response tensors.
A segmentation fault related to DCGM and NSCQ may be encountered during server shutdown on NVSwitch systems. A possible workaround for this issue is to disable the collection of GPU metrics tritonserver --allow-gpu-metrics false ...
vLLM backend currently does not take advantage of the vLLM v0.6 performance improvement when metrics are enabled.
Incorrect results are known to occur when using TensorRT (TRT) Backend for inference using int8 data type for I/O on the Blackwell GPU architecture.
When using TensorRT models, if auto-complete configuration is disabled and is_non_linear_format_io:true for reformat-free tensors is not provided in the model configuration, the model may not load successfully.
When using Python models in decoupled 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.
Restart support was temporarily removed for Python models.
Triton Inference Server with vLLM backend currently does not support running vLLM models with tensor parallelism sizes greater than 1 and the default "distributed_executor_backend" setting when using explicit model control mode. In attempt to load a vllm model (tp > 1) in explicit mode, users could potentially see failure at initialize step: could not acquire lock for <_io.BufferedWriter name='<stdout>'> at interpreter shutdown, possibly due to daemon threads. For the default model control mode, after server shutdown, vllm related sub-processes are not killed. Related vllm issue: vllm-project/vllm#6766 . Please specify "distributed_executor_backend":"ray" in the model.json when deploying vllm models with tensor parallelism > 1.
When loading models with file override, multiple model configuration files are not supported. Users must provide the model configuration by setting parameter "config" : "<JSON>" instead of custom configuration file in the following format: "file:configs/<model-config-name>.pbtxt" : "<base64-encoded-file-content>".
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: https://github.com/pytorch/pytorch/issues/38273
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:
Ubuntu 24.04 builds of the client libraries and examples are included in this release in the attached v2.56.0_ubuntu2404.clients.tar.gz file. The SDK is also available for as an Ubuntu 24.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.
Jetson iGPU Support[!NOTE]
There is no Windows release for 25.03, the latest release is 25.01.
A release of Triton for IGX is provided in the attached tar file: tritonserver2.56.0-igpu.tgz.
10.9.0.34, Onnx Runtime 1.21.0, PyTorch 2.7.0a0+7c8ec84dab.nv25.3, Python 3.12 and as well as ensembles.The tar file contains the Triton server executable and shared libraries and also the C++ and Python client libraries and examples. For more information on how to install and use Triton on JetPack refer to jetson.md.
The wheel for the Python client library is present in the tar file and can be installed by running the following command:
python3 -m pip install --upgrade clients/python/tritonclient-2.56.0-py3-none-manylinux2014_aarch64.whl[all]
The Triton TensorRT-LLM container is built from the 25.03 image nvcr.io/nvidia/tritonserver:25.03-py3-min. Please refer to the support matrix for all dependency versions related to 25.03. However, the packages listed below have ...
The Triton Inference Server provides a cloud inferencing solution optimized for both CPUs and GPUs. The server provides an inference service via an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. For edge deployments, Triton Server is also available as a shared library with an API that allows the full functionality of the server to be included directly in an application.
New Features and ImprovementsPython backend now supports setting and retrieving Inference Response Parameters on InferenceResponse objects on model.py.
Optimized the core Python binding architecture leading to improved OpenAI frontend performance.
Added dynamic sampling parameter handling, improving flexibility and consistency across vllm interactions. Added support for “guided_generation” request parameter for efficient constrained decoding workflows.
Improved Multi-Lora handling in TRTLLM GRPC Client end_to_end_grpc_client.py
GenAI-Perf added the ability to format output using Jinja2 templates.
GenAI-Perf telemetry now supports multiple metric endpoints.
GenAI-Perf now supports increased corpus size, 90x the previously supported size.
GenAI-Perf now supports keys without values as input.
GenAI-Perf fixed the OSL issue due to Performance Analyzer not removing the first 4 bytes from output.
GenAI-Perf added a chat template option for the TRT-LLM engine.
Performance Analyzer fixed TRITON_ENABLE_GPU compile definition bug.
Performance Analyzer bumped minimum required C++ version to C++20.
Performance Analyzer modified to disallow user attempts to use concurrency and warmup with the schedule flag.
The core Python binding may incur an additional D2H and H2D copy if the backend and frontend both specify device memory to be used for response tensors.
A segmentation fault related to DCGM and NSCQ may be encountered during server shutdown on NVSwitch systems. A possible workaround for this issue is to disable the collection of GPU metrics tritonserver --allow-gpu-metrics false ...
vLLM backend currently does not take advantage of the vLLM v0.6 performance improvement when metrics are enabled.
Incorrect results are known to occur when using TensorRT (TRT) Backend for inference using int8 data type for I/O on the Blackwell GPU architecture.
When using TensorRT models, if auto-complete configuration is disabled and is_non_linear_format_io:true for reformat-free tensors is not provided in the model configuration, the model may not load successfully.
When using Python models in decoupled 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.
Restart support was temporarily removed for Python models.
Triton Inference Server with vLLM backend currently does not support running vLLM models with tensor parallelism sizes greater than 1 and the default "distributed_executor_backend" setting when using explicit model control mode. In attempt to load a vllm model (tp > 1) in explicit mode, users could potentially see failure at initialize step: could not acquire lock for <_io.BufferedWriter name='<stdout>'> at interpreter shutdown, possibly due to daemon threads. For the default model control mode, after server shutdown, vllm related sub-processes are not killed. Related vllm issue: vllm-project/vllm#6766 . Please specify "distributed_executor_backend":"ray" in the model.json when deploying vllm models with tensor parallelism > 1.
When loading models with file override, multiple model configuration files are not supported. Users must provide the model configuration by setting parameter "config" : "<JSON>" instead of custom configuration file in the following format: "file:configs/<model-config-name>.pbtxt" : "<base64-encoded-file-content>".
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: https://github.com/pytorch/pytorch/issues/38273
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:
Ubuntu 24.04 builds of the client libraries and examples are included in this release in the attached v2.55.0_ubuntu2404.clients.tar.gz file. The SDK is also available for as an Ubuntu 24.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.
Jetson iGPU Support[!NOTE]
There is no Windows release for 25.02, the latest release is 25.01
A release of Triton for IGX is provided in the attached tar file: tritonserver2.55.0-igpu.tgz.
2.17.0, TensorRT 10.8.0.40, Onnx Runtime 1.20.1, PyTorch 2.6.0a0+ecf3bae40a, Python 3.12 and as well as ensembles.The tar file contains the Triton server executable and shared libraries and also the C++ and Python client libraries and examples. For more information on how to install and use Triton on JetPack refer to jetson.md.
The wheel for the Python client library is present in the tar file and can be installed by running the following command:
python3 -m pip install --upgrade clients/python/tritonclient-2.55.0-py3-none-manylinux2014_aarch64.whl[all]
The Triton TensorRT-LLM container is built from the 25.02 image nvcr.io/nvidia/tritonserver:25.02-py3-min. Please refer to the [support matr...
The Triton Inference Server provides a cloud inferencing solution optimized for both CPUs and GPUs. The server provides an inference service via an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. For edge deployments, Triton Server is also available as a shared library with an API that allows the full functionality of the server to be included directly in an application.
New Features and ImprovementsStarting with the 25.01 release, Triton Inference Server supports Blackwell GPU architectures.
Starting from 25.01, the vLLM container shipped by Triton is NVIDIA optimized. Users who wish to use the public version of vLLM can continue to build a Triton-vLLM container on their end.
Fixed a bug when passing the correlation ID of string type to python_backend. Added datatype checks to correlation ID values.
vLLM backend can now take advantage of the vLLM v0.6 performance improvement by communicating with the vLLM engine via ZMQ.
GenAI-Perf now provides the exact input sequence length requested for synthetic text generation
GenAI-Perf supports the creation of a prefix pool to emulate system prompts via --num-system-prompts and --system-prompt-length
GenAI-Perf improved error visibility via returning more detailed errors when OpenAI frontends return an error or metric generation fails
GenAI-Perf reports time-to-second-token and request count in its metrics
GenAI-Perf allows the use of a custom tokenizer in its “compare” subcommand for comparing multiple profiles
GenAI-Perf natively supports --request-count for sending a specific number of requests and --header for sending a list of headers with every request
Model Analyzer functionality has been migrated to GenAI-Perf via the “analyze” subcommand, enabling the tool to sweep and find the optimal model configuration
A bytes appending bug was fixed in GenAI-Perf, resulting in more accurate output sequence lengths for Triton
Update February 12th, 2025: Triton Windows release now has CUDA context sharing support in the TensorRT Backend
A segmentation fault related to DCGM and NSCQ may be encountered during server shutdown on NVSwitch systems. A possible workaround for this issue is to disable the collection of GPU metrics tritonserver --allow-gpu-metrics false ...
vLLM backend currently does not take advantage of the vLLM v0.6 performance improvement when metrics are enabled.
Please note, that the vllm version provided in 25.01 container is 0.6.3.post1. Due to some issues with vllm library versioning, vllm.__version__ displays 0.6.3.
Incorrect results are known to occur when using TensorRT (TRT) Backend for inference using int8 data type for I/O on the Blackwell GPU architecture.
When running Torch TRT models, the output may differ from running the same model on a previous release.
When using TensorRT models, if auto-complete configuration is disabled and is_non_linear_format_io:true for reformat-free tensors is not provided in the model configuration, the model may not load successfully.
When using Python models in decoupled 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.
Restart support was temporarily removed for Python models.
Triton Inference Server with vLLM backend currently does not support running vLLM models with tensor parallelism sizes greater than 1 and the default "distributed_executor_backend" setting when using explicit model control mode. In attempt to load a vllm model (tp > 1) in explicit mode, users could potentially see failure at initialize step: could not acquire lock for <_io.BufferedWriter name='<stdout>'> at interpreter shutdown, possibly due to daemon threads. For the default model control mode, after server shutdown, vllm related sub-processes are not killed. Related vllm issue: vllm-project/vllm#6766 . Please specify "distributed_executor_backend":"ray" in the model.json when deploying vllm models with tensor parallelism > 1.
When loading models with file override, multiple model configuration files are not supported. Users must provide the model configuration by setting parameter "config" : "<JSON>" instead of custom configuration file in the following format: "file:configs/<model-config-name>.pbtxt" : "<base64-encoded-file-content>".
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: https://github.com/pytorch/pytorch/issues/38273
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:
Ubuntu 24.04 builds of the client libraries and examples are included in this release in the attached v2.54.0_ubuntu2404.clients.tar.gz file. The SDK is also available for as an Ubuntu 24.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.
A beta release of Triton for Windows is provided in the attached file: tritonserver2.54.0-win.zip. This is a beta release so functionality is limited and performance is not optimized. Additional features and improved performance will be provided in future releases. Specifically in this release:
*ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.20.1. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
OpenVINO models are supported. The OpenVINO version is 2024.5.0.
Prometheus metrics endpoint is not supported.
System and CUDA shared memory are not supported.
Known Issues
To use the Windows version of Triton, you must install all the necessary dependencies on your Windows system. These dependencies are available in the Dockerfile.win10.min. The Dockerfile includes the following CUDA-related components:
Python 3.12.3
CUDA 12.6.3
cuDNN 9.6.0.74
TensorRT 10.7.0.23
A release of Triton for IGX is ...
Read more Release 2.53.0 corresponding to NGC container 24.12 Triton Inference ServerThe Triton Inference Server provides a cloud inferencing solution optimized for both CPUs and GPUs. The server provides an inference service via an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. For edge deployments, Triton Server is also available as a shared library with an API that allows the full functionality of the server to be included directly in an application.
New Features and ImprovementsvLLM backend health check may be optionally enabled which unloads the model if the vLLM engine health check failed.
vLLM backend supports sending additional outputs from vLLM if requested.
Improved server stability during the gRPC client cancellation.
Perf Analyzer: Added trtllm multi node process support.
Windows executables and DLLs are signed by NVIDIA. This should remove the un-trusted software popup when starting Triton outside of administrator mode.
Triton on Windows supports long path notation for model repositories
Triton on Windows supports wide character encoding, UTF-16, for model repositories
To build the Llama 3.1 engine inside the 24.09-trtllm-python-py3 image, make sure to upgrade the transformer library to 4.43+ due to the bug in 4.43.x. One option to do so is to run pip install -U transformers. For more information, please refer to the discussion: NVIDIA/TensorRT-LLM#2121
Triton vLLM container comes with the vLLM version, which has a known vulnerability: GHSA-w2r7-9579-27hf. Note, that the affected code is not invoked at runtime, therefore the Triton vLLM container is not affected by this issue.
When running Torch TRT models, the output may differ from running the same model on a previous release.
When using TensorRT models, if auto-complete configuration is disabled and is_non_linear_format_io:true for reformat-free tensors is not provided in the model configuration, the model may not load successfully.
When using Python models in decoupled 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.
Restart support was temporarily removed for Python models.
Triton TensorRT-LLM Backend container image uses TensorRT-LLM version 0.16.0 and built out of nvcr.io/nvidia/tritonserver:24.11-py3-min. Please refer to the Triton TRT-LLM Container Support Matrix section in the GitHub release note for more details.
Triton Inference Server with vLLM backend currently does not support running vLLM models with tensor parallelism sizes greater than 1 and the default "distributed_executor_backend" setting when using explicit model control mode. In attempt to load a vllm model (tp > 1) in explicit mode, users could potentially see failure at initialize step: could not acquire lock for <_io.BufferedWriter name='<stdout>'> at interpreter shutdown, possibly due to daemon threads. For the default model control mode, after server shutdown, vllm related sub-processes are not killed. Related vllm issue: https://github.com/vllm-project/vllm/issues/6766 . Please specify "distributed_executor_backend":"ray" in the model.json` when deploying vllm models with tensor parallelism > 1.
When loading models with file override, multiple model configuration files are not supported. Users must provide the model configuration by setting parameter "config" : "" instead of custom configuration file in the following format:"file:configs/.pbtxt" : "".
Perf Analyzer no longer supports --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: https://github.com/pytorch/pytorch/issues/38273
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:
Ubuntu 24.04 builds of the client libraries and examples are included in this release in the attached v2.53.0_ubuntu2404.clients.tar.gz file. The SDK is also available for as an Ubuntu 24.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.
Important
This release of Triton on Windows includes features specific to the Windows platform and does not affect the 2.53.0 release for other platforms. This is released as a patch to 2.53.0 because of the differing feature commits.
A beta release of Triton for Windows is provided in the attached file: tritonserver2.53.1-win.zip. This is a beta release so functionality is limited and performance is not optimized. Additional features and improved performance will be provided in future releases. Specifically in this release:
*ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.20.1. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
OpenVINO models are supported. The OpenVINO version is 2024.5.0.
Prometheus metrics endpoint is not supported.
System and CUDA shared memory are not supported.
Known Issues
To use the Windows version of Triton, you must install all the necessary dependencies on your Windows system. These dependencies are available in the Dockerfile.win10.min. The Dockerfile includes the following CUDA-related components:
Python 3.12.3
CUDA 12.6.3
cuDNN 9.6.0.74
TensorRT 10.7.0.23
A release of Triton for IGX is provided in the attached tar file: tritonserver2.53.0-igpu.tgz.
2.17.0, TensorRT 10.7.0.23, Onnx Runtime 1.20.1, PyTorch 2.6.0a0+df5bbc0, Python 3.12 and as well as ensembles.The Triton Inference Server provides a cloud inferencing solution optimized for both CPUs and GPUs. The server provides an inference service via an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. For edge deployments, Triton Server is also available as a shared library with an API that allows the full functionality of the server to be included directly in an application.
New Features and ImprovementsConceptual Guides were enhanced with a comprehensive tutorial on Semantic Caching optimization for LLM workloads.
Triton Metrics:
--metrics-config histogram_latencies=true [user_guide] .model_metrics that allows overriding default buckets for histogram metric families.TensorFlow backend may leak memory due to a known issue with the cuDNN library shipped with the container.
The latest GenAI-Perf package on pypi.org is version 0.0.9dev while the latest Triton SDK container (24.11) contains GenAI-Perf version 0.0.8.
Numpy 2.x is not currently supported for Python Backend models and may cause them to return empty tensors unxpectedly, please use Numpy 1.x until support is added.
Triton vLLM container comes with the vLLM version, which has a known vulnerability: GHSA-w2r7-9579-27hf. Note, that the affected code is not invoked at runtime, therefore the Triton vLLM container is not affected by this issue.
When running Torch TRT models, the output may differ from running the same model on a previous release.
When using TensorRT models, if auto-complete configuration is disabled and is_non_linear_format_io:true for reformat-free tensors is not provided in the model configuration, the model may not load successfully.
When using Python models in decoupled 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.
Restart support was temporarily removed for Python models.
Triton TensorRT-LLM Backend container image uses TensorRT-LLM version 0.15.0 and built out of nvcr.io/nvidia/tritonserver:24.10-py3-min. Please refer to the Triton TRT-LLM Container Support Matrix section in the GitHub release note for more details.
Triton Inference Server with vLLM backend currently does not support running vLLM models with tensor parallelism sizes greater than 1 and the default "distributed_executor_backend" setting when using explicit model control mode. In attempt to load a vllm model (tp > 1) in explicit mode, users could potentially see failure at initialize step: could not acquire lock for <_io.BufferedWriter name='<stdout>'> at interpreter shutdown, possibly due to daemon threads. For the default model control mode, after server shutdown, vllm related sub-processes are not killed. Related vllm issue: https://github.com/vllm-project/vllm/issues/6766 . Please specify "distributed_executor_backend":"ray" in the model.json` when deploying vllm models with tensor parallelism > 1.
When loading models with file override, multiple model configuration files are not supported. Users must provide the model configuration by setting parameter "config" : "" instead of custom configuration file in the following format:"file:configs/.pbtxt" : "".
Perf Analyzer no longer supports --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: https://github.com/pytorch/pytorch/issues/38273
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:
Ubuntu 22.04 builds of the client libraries and examples are included in this release in the attached v2.52.0_ubuntu22.04.clients.tar.gz file. The SDK is also available for as an Ubuntu 22.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.
Note
There is no Windows release for 24.11, the latest release is 24.10.
Jetson iGPU SupportA release of Triton for IGX is provided in the attached tar file: tritonserver2.52.0-igpu.tgz.
2.17.0, TensorRT 10.6.0.26, Onnx Runtime 1.19.2, PyTorch 2.6.0a0+df5bbc0, Python 3.12 and as well as ensembles.The tar file contains the Triton server executable and shared libraries and also the C++ and Python client libraries and examples. For more information on how to install and use Triton on JetPack refer to jetson.md.
The wheel for the Python client library is present in the tar file and can be installed by running the following command:
python3 -m pip install --upgrade clients/python/tritonclient-2.52.0-py3-none-manylinux2014_aarch64.whl[all]
The Triton TensorRT-LLM container is built from the 24.10 image nvcr.io/nvidia/tritonserver:24.10-py3-min. Please refer to the support matrix for all dependency versions related to 24.10. However, the packages listed below have different versions than those specified in the support matrix.
The Triton Inference Server provides a cloud inferencing solution optimized for both CPUs and GPUs. The server provides an inference service via an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. For edge deployments, Triton Server is also available as a shared library with an API that allows the full functionality of the server to be included directly in an application.
New Features and ImprovementsNumpy 2.x is not currently supported for Python Backend models and may cause them to return empty tensors unxpectedly, please use Numpy 1.x until support is added.
To build the Llama 3.1 engine inside the 24.09-trtllm-python-py3 image, make sure to upgrade the transformer library to 4.43+ due to the bug in 4.43.x. One option to do so is to run pip install -U transformers. For more information, please refer to the discussion: NVIDIA/TensorRT-LLM#2121
Triton vLLM container comes with the vLLM version, which has a known vulnerability: GHSA-w2r7-9579-27hf. Note, that the affected code is not invoked at runtime, therefore the Triton vLLM container is not affected by this issue.
When running Torch TRT models, the output may differ from running the same model on a previous release.
When using TensorRT models, if auto-complete configuration is disabled and is_non_linear_format_io:true for reformat-free tensors is not provided in the model configuration, the model may not load successfully.
When using Python models in decoupled 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.
Restart support was temporarily removed for Python models.
Triton TensorRT-LLM Backend container image uses TensorRT-LLM version 0.14.0 and built out of nvcr.io/nvidia/tritonserver:24.07-py3-min. Please refer to the Triton TRT-LLM Container Support Matrix section in the GitHub release note for more details.
Triton Inference Server with vLLM backend currently does not support running vLLM models with tensor parallelism sizes greater than 1 and the default "distributed_executor_backend" setting when using explicit model control mode. In attempt to load a vllm model (tp > 1) in explicit mode, users could potentially see failure at initialize step: could not acquire lock for <_io.BufferedWriter name='<stdout>'> at interpreter shutdown, possibly due to daemon threads. For the default model control mode, after server shutdown, vllm related sub-processes are not killed. Related vllm issue: https://github.com/vllm-project/vllm/issues/6766 . Please specify "distributed_executor_backend":"ray" in the model.json` when deploying vllm models with tensor parallelism > 1.
When loading models with file override, multiple model configuration files are not supported. Users must provide the model configuration by setting parameter "config" : "" instead of custom configuration file in the following format:"file:configs/.pbtxt" : "".
Perf Analyzer no longer supports --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: https://github.com/pytorch/pytorch/issues/38273
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:
Ubuntu 22.04 builds of the client libraries and examples are included in this release in the attached v2.51.0_ubuntu22.04.clients.tar.gz file. The SDK is also available for as an Ubuntu 22.04 based NGC Container. The SDK container includes the client libraries and examples, Performance Analyzer and Model Analyzer. Some components are also available in the tritonclient pip package. See Getting the Client Libraries for more information on each of these options.
For Windows, the client libraries and some examples are available in the attached tritonserver2.51.0-sdk-win.zip file.
A beta release of Triton for Windows is provided in the attached file:tritonserver2.51.0-win.zip. This is a beta release so functionality is limited and performance is not optimized. Additional features and improved performance will be provided in future releases. Specifically in this release:
HTTP/REST and GRPC endpoints are supported.
ONNX models are supported by the ONNX Runtime backend. The ONNX Runtime version is 1.19.2. The CPU, CUDA, and TensorRT execution providers are supported. The OpenVINO execution provider is not supported.
OpenVINO models are supported. The OpenVINO version is 2024.4.0.
Prometheus metrics endpoint is not supported.
System and CUDA shared memory are not supported.
To use the Windows version of Triton, you must install all the necessary dependencies on your Windows system. These dependencies are available in the Dockerfile.win10.min. The Dockerfile includes the following CUDA-related components:
Python 3.10.11
CUDA 12.5.1
cuDNN 9.5.0.50
TensorRT 10.5.0.18
A release of Triton for IGX is provided in the attached tar file: tritonserver2.51.0-igpu.tgz.
2.16.1, TensorRT 10.5.0.18, Onnx Runtime 1.19.2, PyTorch 2.5.0a0+e000cf0, Python 3.10 and as well as ensembles.The tar file contains the Triton server executable and shared libraries and also the C++ and Python client libraries and examples. For more information on how to install and use Triton on JetPack refer to jetson.md.
The wheel for the Python client library is present in the tar file and can be installed by running the following command:
python3 -m pip install --upgrade clients/python/tritonclient-2.51.0-py3-none-manylinux2014_aa...
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