NVIDIA TensorRT Inference Server

The NVIDIA TensorRT Inference Server provides a cloud inferencing solution optimized for NVIDIA 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.

What’s New In 0.11.0 Beta

  • Variable-size input and output tensor support. Models that support variable-size input tensors and produce variable-size output tensors are now supported in the model configuration by using a dimension size of -1 for those dimensions that can take on any size.
  • String datatype support. For TensorFlow models and custom backends, input and output tensors can contain strings.
  • Improved support for non-GPU systems. The inference server will run correctly on systems that do not contain GPUs and that do not have nvidia-docker or CUDA installed.


  • Multiple framework support. The server can manage any number and mix of models (limited by system disk and memory resources). Supports TensorRT, TensorFlow GraphDef, TensorFlow SavedModel and Caffe2 NetDef model formats. Also supports TensorFlow-TensorRT integrated models.
  • Custom backend support. The inference server allows individual models to be implemented with custom backends instead of by a deep-learning framework. With a custom backend a model can implement any logic desired, while still benefiting from the GPU support, concurrent execution, dynamic batching and other features provided by the server.
  • The inference server monitors the model repository for any change and dynamically reloads the model(s) when necessary, without requiring a server restart. Models and model versions can be added and removed, and model configurations can be modified while the server is running.
  • Multi-GPU support. The server can distribute inferencing across all system GPUs.
  • Concurrent model execution support. Multiple models (or multiple instances of the same model) can run simultaneously on the same GPU.
  • Batching support. For models that support batching, the server can accept requests for a batch of inputs and respond with the corresponding batch of outputs. The inference server also supports dynamic batching where individual inference requests are dynamically combined together to improve inference throughput. Dynamic batching is transparent to the client requesting inference.
  • Model repositories may reside on a locally accessible file system (e.g. NFS) or in Google Cloud Storage.
  • Readiness and liveness health endpoints suitable for any orchestration or deployment framework, such as Kubernetes.
  • Metrics indicating GPU utiliization, server throughput, and server latency.

Indices and tables