New AI software tools include Riva Customer Voice, TensorRT, Triton Inference Server, Merlin, NeMo Framework, and DeepStream.
Overview
At NVIDIA GTC, new AI tools and technologies were announced, including NVIDIA Riva for speech applications, TensorRT 8.2 for deep learning inference, and NVIDIA Triton Inference Server 2.15 for scalable AI production. These advancements aim to enhance real-time applications, optimize model performance, and improve interoperability in AI workflows.
What You'll Learn
1
How to create a custom voice using NVIDIA Riva
2
Why TensorRT 8.2 can improve inference speed in deep learning applications
3
How to deploy models at scale using NVIDIA Triton Inference Server
4
When to use the NeMo Framework for large-scale language models
5
How to utilize DeepStream 6.0 for video analytics applications
Key Questions Answered
What is NVIDIA Riva and how can it be used for speech applications?
NVIDIA Riva is a software tool that enables developers to create real-time speech applications. It features a Custom Voice option allowing enterprises to generate unique voices with just 30 minutes of audio data, and it supports multiple languages for speech recognition.
How does TensorRT 8.2 enhance deep learning inference performance?
TensorRT 8.2 optimizes inference applications to run billion-parameter language models in real-time, achieving up to 21x faster performance compared to CPUs. It integrates seamlessly with PyTorch and TensorFlow, allowing developers to enhance performance with minimal code changes.
What new features are included in NVIDIA Triton Inference Server 2.15?
NVIDIA Triton Inference Server 2.15 introduces a Model Analyzer for optimizing execution parameters, supports multi-GPU multinode distributed inference, and is available on major public clouds. It also adds compatibility for AI inference workloads on Arm CPUs.
What advancements does the NeMo Framework provide for language models?
The NeMo Framework allows enterprises to train and scale large language models with trillions of parameters, utilizing advanced parallelization techniques and automated data curation tasks. It supports training models like Megatron 530B efficiently across multiple GPUs and nodes.
Key Statistics & Figures
Performance improvement with TensorRT
21x faster
TensorRT 8.2 optimizes inference for T5 and GPT-2 models compared to CPUs.
Time to create a new neural voice with Riva
30 minutes
Enterprises can generate a unique voice representation using just 30 minutes of audio data.
Technologies & Tools
Speech Processing
Nvidia Riva
Used for building real-time speech applications.
Deep Learning
Tensorrt
High-performance inference optimizer and runtime engine.
Inference Serving
Nvidia Triton Inference Server
Open-source software for fast and scalable AI production.
Machine Learning
Nemo
Framework for developing large-scale language models.
Video Analytics
Deepstream
Toolkit for building high-performance video analytics applications.
Key Actionable Insights
1Leverage NVIDIA Riva's Custom Voice feature to create a brand-specific voice for applications.This capability allows businesses to enhance user engagement and brand recognition through unique voice interactions, making it particularly useful for customer service and virtual assistants.
2Utilize TensorRT 8.2 to significantly reduce inference times in deep learning models.By integrating TensorRT, developers can achieve real-time performance for complex models, which is crucial for applications requiring rapid response times, such as chatbots and translation services.
3Implement NVIDIA Triton Inference Server to streamline the deployment of AI models at scale.Triton's support for multi-GPU and multinode distributed inference ensures that large-scale applications can maintain performance and reliability, which is essential for production environments.
Related Concepts
AI/ML Frameworks
Deep Learning Optimization Techniques
Real-time Speech Processing
Video Analytics Applications