Ankit Patel
WWC24 - Ankit Patel - Unlocking the Future Breakthrough Application Performance and Capabilities with NVIDIA
#1about 3 minutes
Understanding accelerated computing and GPU parallelism
Accelerated computing offloads parallelizable tasks from the CPU to specialized GPU cores, executing them simultaneously for a massive speedup.
#2about 2 minutes
Calculating the cost and power savings of GPUs
While a GPU-accelerated system costs more upfront, it can replace hundreds of CPU systems for parallel workloads, leading to significant cost and power savings.
#3about 4 minutes
Using NVIDIA libraries to easily accelerate applications
NVIDIA provides domain-specific libraries like cuDF that allow developers to accelerate their code, such as pandas dataframes, with minimal changes.
#4about 3 minutes
Shifting from traditional code to AI-powered logic
Modern AI development replaces complex, hard-coded logic with prompts to large language models, changing how developers implement functions like sentiment analysis.
#5about 3 minutes
Composing multiple AI models for complex tasks
Developers can now create sophisticated applications by chaining multiple AI models together, such as using a vision model's output to trigger an LLM that calls a tool.
#6about 2 minutes
Deploying enterprise AI applications with NVIDIA NIM
NVIDIA NIM provides enterprise-grade microservices for deploying AI models with features like runtime optimization, stable APIs, and Kubernetes integration.
#7about 4 minutes
Accessing NVIDIA's developer programs and training
NVIDIA offers a developer program with access to libraries, NIMs for local development, and free training courses through the Deep Learning Institute.
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The evolution of GPUs from graphics to AI computing
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