AI inference can deliver faster, more accurate predictions to organizations of all sizes—but building a platform for production AI inference is hard. Real-world use cases require different types of AI model architectures, and the models can contain hundreds of millions of parameters. Models are trained in different frameworks (TensorFlow, PyTorch, XGBoost, Python, and others) and have different formats. Applications have different requirements (real-time low latency, high-throughput batch, or streaming inputs), and then there are different execution environments (CPUs, GPUs, in the cloud, on premises, at the edge). High-performance inference on specific hardware or in-framework is challenging because of competing constraints like latency, accuracy, throughput, and memory size that modern AI applications demand.

Join our webinar to explore how NVIDIA’s inference solution, including open-source NVIDIA Triton™ Inference Server and NVIDIA^® TensorRT™, delivers fast and scalable AI inference in production.

• How to optimize, deploy, and scale AI models in production using Triton Inference Server and TensorRT

• How Triton streamlines inference serving across multiple frameworks, across different query types (real-time, batch, streaming), on CPUs and GPUs, and with a model analyzer for efficient deployment

• How to standardize workflows to optimize models using TensorRT and framework Integrations with PyTorch and TensorFlow

• About real-world use cases of customers and the benefits they’re seeing with Triton and TensorRT

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