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Horovod vs NVIDIA Triton Inference Server

Horovod

horovod.ai

NVIDIA Triton Inference Server

developer.nvidia.com

Side by side

FigureHorovodNVIDIA Triton Inference Server
Price fromFree$375/mo
Free planYesYes
Free trial—Yes
Open source——
Windows—Yes
macOSYes—
LinuxYesYes
APIYesYes
Self-hostedYesYes
Plans published13
Spec score · Deep Learning Software8.1 (#11)8.0 (#13)
PurposeHorovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch and Apache MXNet.Dynamo-Triton is open-source inference-serving software for deploying, running, and scaling AI models from multiple frameworks on GPU- or CPU-based infrastructure.
SecurityThe documentation includes a security section for Horovod on Spark; the opened pages make no security certification or compliance claim.NVIDIA's secure-deployment guide says solution security is the deployer's responsibility, dynamic model repository updates are disabled by default, and Triton does not sandbox arbitrary model or backend code.

Plans

Horovod

  • HorovodFree

Horovod pricing →

NVIDIA Triton Inference Server

  • Open-source developmentFree
  • NVIDIA AI Enterprise cloud productionPriced on request
  • NVIDIA AI Enterprise subscription$4,500 a year

NVIDIA Triton Inference Server pricing →

In their own words

Horovod

Horovod has no write-up on Specifiction yet; its figures are above.

Visit horovod.ai

NVIDIA Triton Inference Server

NVIDIA Triton Inference Server is open-source software for deploying and serving AI models on GPU- or CPU-based infrastructure. It supports TensorRT, PyTorch, ONNX, OpenVINO, Python and RAPIDS FIL, with model formats including TensorRT Plan, ONNX, TensorFlow GraphDef and SavedModel, PyTorch TorchScript and PyTorch 2.0. Triton serves real-time, batched, ensemble and audio/video streaming workloads. Dynamic batching and concurrent model execution are among its performance features. Requests can use HTTP/REST, gRPC or the C API, with a Java API for in-process use. Triton integrates with Kubernetes for scaling and Prometheus for monitoring. Deployment options include NVIDIA GPUs, other accelerators, x86 and ARM CPUs, cloud, data centers, edge and embedded systems. NVIDIA lists Linux containers for x86 and Arm, plus Windows and Jetson JetPack binaries. Development code and containers are free; production options include a cloud marketplace plan billed pay as you go and a subscription listed at $4,500 a year per GPU. NVIDIA says deployers are responsible for security, and Triton does not sandbox arbitrary model or backend code.

Visit developer.nvidia.com