NVIDIA DeepStream SDK
NVIDIA DeepStream’s multi-platform support gives you a faster, easier way to develop and deploy real-time video streaming pipelines for generative AI agents and applications. You can even deploy them on premises, at the edge, and in the cloud with just the click of a button.
What is NVIDIA DeepStream?
The NVIDIA DeepStream SDK is a comprehensive real-time streaming analytics toolkit based on GStreamer for AI-based multi-sensor processing, video, audio, and image understanding. It’s ideal for developers, software partners, startups, and OEMs building vision AI agents and applications and services for a wide range of industries like smart cities, retail, manufacturing, and more.
You can now create and deploy stream-processing pipelines that incorporate generative AI and other complex processing tasks like multi-camera tracking in minutes. To further accelerate development, DeepStream is also part of the NVIDIA Metropolis Blueprint for Video Search and Summarization (VSS). This sample architecture for building visual AI agents can extract valuable insights from massive volumes of industrial video sensor data in real time.
DeepStream is an integral part of NVIDIA Metropolis, the platform for building end-to-end vision AI agents and applications that transform pixel and sensor data into actionable insights.
Benefits
Rapidly Deploy AI From the Cloud to the Edge
The DeepStream SDK provides a complete video stream processing, ingestion, multi-camera, tracking pipeline that’s 100% NVIDIA GPU-accelerated. It’s ideal for a wide range of use cases across industries such as manufacturing, logistics, retail, and more.
Reduce Development Time to Minutes
DeepStream Inference Builder simplifies the development process with declarative application definitions and easy addition of API endpoints for your application.
Real-Time Insights
Extract rich metadata in real time from sensor data such as images, video, and lidar.
Achieve the Lowest Total Cost of Ownership With NVIDIA GPUs
Increase stream density, maximize performance, and minimize TCO by deploying AI models with DeepStream on NVIDIA hardware.
Multiple Programming Options
Create powerful vision AI applications using C/C++ and Python.
Unique Capabilities
Accelerate Vision AI Development With 40+ GPU-Accelerated Plug-Ins
DeepStream kickstarts the development of seamless real-time streaming pipelines for AI-based video, audio, and image analytics. It ships with 40+ hardware-accelerated plug-ins and 30+ sample applications and extensions to optimize pre/post processing, inference, multi-camera tracking, message brokers, and more.
Inference Builder turns AI model ideas into production-ready services with ease. Developers can now go from YAML config to deployment in minutes—automating data flow, preprocessing, model execution, and API integration.
DeepStream Service Maker simplifies the development process by abstracting the complexities of GStreamer to easily build C++ object-oriented applications. Use Service Maker to build complete DeepStream pipelines with a few lines of code
DeepStream Libraries powered by NVIDIA? CV-CUDA?, NvImageCodec, and PyNvVideoCodec offer low-level GPU-accelerated operations to optimize pre- and post- stages of vision AI pipelines.
Enable Multi-Camera Tracking Across
a Range of Cameras
Multiview 3D tracking (MV3DT), an extension of DeepStream NvTracker, enables distributed, real-time 3D tracking across networks of cameras. It works seamlessly with both 2D and 3D detectors, supporting a wide range of use cases. DeepStream automatically assigns unique IDs for new objects, preserving identity through occlusions and handovers.
For precise multi-camera tracking, DeepStream includes a new calibration tool that aligns multiple cameras to the deployment floor plan simultaneously. This reduces manual effort and ensures consistent, accurate results.
Build End-to-End AI Solutions
Speed up overall development efforts and unlock greater real-time performance by building end-to-end vision AI applications with NVIDIA Metropolis. Start with production-quality vision AI models, adapt and optimize them with the NVIDIA TAO Toolkit, and deploy using DeepStream. Use the Metropolis VSS Blueprint to build visual AI agents that can process thousands of live videos simultaneously to drive insights and automation.
Get incredible flexibility—from rapid prototyping to full production-level solutions—and choose your inference path. With native integration to NVIDIA Triton? Inference Server, you can deploy models in native frameworks such as PyTorch and TensorFlow for inference. For high-throughput inference, use NVIDIA TensorRT to achieve the best possible performance.
Enjoy Seamless Development From Edge to Cloud
DeepStream’s off-the-shelf containers let you build once and deploy anywhere—on clouds, workstations with NVIDIA GPUs, or NVIDIA Jetson? devices. With the DeepStream Container Builder and NGC containers, you can easily create scalable, high-performance AI applications managed with Kubernetes and Helm.
DeepStream REST-APIs also let you manage multiple parameters at run-time, simplifying the creation of SaaS solutions. With a standard REST-API interface, you can build web portals for control and configuration or integrate into your existing applications.
Get Production-Ready
DeepStream is available as a part of NVIDIA AI Enterprise, an end-to-end, secure, cloud-native AI software platform optimized to accelerate enterprises to the leading edge of AI.
NVIDIA AI Enterprise delivers validation and integration for NVIDIA AI open-source software, access to AI solution workflows to speed time to production, certifications to deploy AI everywhere, and enterprise-grade support, security, and API stability to mitigate the potential risks of open-source software.
Explore Multiple Programming Options
Inference Builder
Streamline the entire workflow with a simple YAML configuration—automating data flow, preprocessing, model execution, and API integration.
Python
Construct DeepStream pipelines using Gst Python, the GStreamer framework's Python bindings. The source code for the binding and Python sample applications are available on GitHub.
C/C++
Create applications in C/C++, interact directly with GStreamer and DeepStream plug-ins, and use reference applications and templates.
Improve Accuracy and Real-Time Performance
DeepStream offers exceptional throughput for a wide variety of object detection, image processing, and instance segmentation AI models. The following table shows the end-to-end application performance from data ingestion, decoding, and image processing to inference. It takes multiple 1080p/30fps streams as input. Note that running on the DLAs for Jetson devices frees up the GPU for other tasks. For performance best practices, watch this video tutorial.
| Foundation Model | Tracker | Precision | Jetson Thor | L40S | A6000 | B200 | RTX PRO WS | RTX PRO SE |
|---|---|---|---|---|---|---|---|---|
| C-RADIO-Base | No Tracker | FP16 | 1242 | 2880 | 2568 | 8352 | 3025 | 3864 |
| NV-DinoV2-Large | No Tracker | FP16 | 412 | 797 | 616 | 3552 | 895 | 1330 |
| RT-DETR + C-RADIO-Base | No Tracker | FP16 | 194 | 609 | 546 | 1888 | 630 | 920 |
| RT-DETR + C-RADIO-Base | NvDCF | FP16 | 161 | 618 | 534 | 1824 | 627 | 848 |
| RT-DETR + C-RADIO-Base | MV3DT | FP16 | 183 | 343 | 497 | 1128 | 598 | 624 |
| TrafficCamNet Transformer Lite | NvDCF | FP16 | 157 | 685 | 584 | 1200 | 785 | 928 |
| Peoplenet (2.6.3) | MV3DT | FP16 | 617 | 486 | 552 | 4320 | 860 | 852 |
| Grounding-DINO | No Tracker | FP16 | 24 | 98 | 101 | 208 | 132 | 158 |
| SegFormer + C-RADIO-Base | No Tracker | FP16 | 135 | 870 | 884 | 1508 | 157 | 1060 |
| Mask2Former + SWIN | No Tracker | FP16 | 26 | 94 | 63 | 173 | 68 | 102 |
The DeepStream SDK lets you apply AI to streaming video and simultaneously optimize video decode/encode, image scaling, and conversion and edge-to-cloud connectivity for complete end-to-end performance optimization.
To learn more about the performance using DeepStream, check the documentation.
Read Customer Stories
Optimizing Operations at Bengaluru Airport
Industry.AI used the NVIDIA Metropolis stack, including DeepStream, to increase the safety and efficiency of the airport. Using vision AI, it was able to track abandoned baggage, flag long passenger queues, and alert security teams of potential issues.
Enhancing Distribution Center Operation
KoiReader developed an AI-powered machine vision solution using NVIDIA developer tools that included the DeepStream SDK to help PepsiCo achieve precision and efficiency in dynamic distribution environments.
Scaling AI-Powered Smart Spaces
FYMA used NVIDIA DeepStream and NVIDIA Triton? to improve AI-powered space analytics with frame rates exceeding previous benchmarks by 10x and accuracy by 3x.
General FAQ
DeepStream is a closed-source SDK. Note that sources for all reference applications and several plugins are available. DeepStream Inference Builder will be open -source and available on GitHub.
The DeepStream SDK can be used to build end-to-end AI-powered applications to analyze video and sensor data. Some popular use cases are retail analytics, parking management, managing logistics, optical inspection, robotics, and sports analytics.
See the Platforms and OS compatibility table.
Yes, that’s now possible with the integration of the Triton Inference server. Also with DeepStream 6.1.1, applications can communicate with independent/remote instances of Triton Inference Server using gPRC.
DeepStream supports several popular models out of the box. For instance, DeepStream supports all NVIDIA TAO models and ships with an example to run YOLO models.
Yes, DeepStream 8.0 or later supports NVIDIA Blackwell architecture.
Yes, audio is supported with DeepStream SDK. To get started, download the software and review the reference audio and Automatic Speech Recognition (ASR) applications. Learn more by reading the ASR DeepStream Plugin
Build high-performance vision AI apps and services using the DeepStream SDK.