TorchServe vs NVIDIA Triton Inference Server

TorchServe

The PyTorch ecosystem's equivalent of Tensorflow Serving is TorchServe. It's optimized for serving PyTorch models across several domains including vision, NLP, tabular data, and audio.

Like Tensorflow Serving, it is designed for large-scale cloud deployments and can require custom configuration for things like pre- and post-processing and deploying multiple models. Because of its wide mandate it lacks many vision-specific features (like video streaming).

Chose TorchServe if: you're looking for a way to scale and customize the deployment of your PyTorch models and don't need vision-specific functionality.

NVIDIA Triton Inference Server

Triton is a powerhouse tool for machine learning experts to deploy ML models at scale. Its primary focus is on extremely optimized pipelines that run efficiently on NVIDIA hardware. It can be tough to use, tradingoff simplicity and a quick development cycle for raw speed and isgeared towards expert users. It can chain models together, but doingso is a rigid and manual process.

Make any camera an AI camera with Inference

Inference turns any computer or edge device into a command center for your computer vision projects.

  • 🛠️ Self-host your own fine-tuned models
  • 🧠 Access the latest and greatest foundation models (like Florence-2, CLIP, and SAM2)
  • 🤝 Use Workflows to track, count, time, measure, and visualize
  • 👁️ Combine ML with traditional CV methods (like OCR, Barcode Reading, QR, and template matching)
  • 📈 Monitor, record, and analyze predictions
  • 🎥 Manage cameras and video streams
  • 📬 Send notifications when events happen
  • 🛜 Connect with external systems and APIs
  • 🔗 Extend with your own code and models
  • 🚀 Deploy production systems at scale

Get started today.

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