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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Matching moments
05:12 MIN
Boosting Python performance with the Nvidia CUDA ecosystem
The weekly developer show: Boosting Python with CUDA, CSS Updates & Navigating New Tech Stacks
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01:40 MIN
The rise of general-purpose GPU computing
Accelerating Python on GPUs
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01:04 MIN
NVIDIA's platform for the end-to-end AI workflow
Trends, Challenges and Best Practices for AI at the Edge
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01:37 MIN
Introduction to large-scale AI infrastructure challenges
Your Next AI Needs 10,000 GPUs. Now What?
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01:57 MIN
Highlighting impactful contributions and the rise of open models
Open Source: The Engine of Innovation in the Digital Age
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03:22 MIN
Using NVIDIA's full-stack platform for developers
Pioneering AI Assistants in Banking
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02:21 MIN
How GPUs evolved from graphics to AI powerhouses
Accelerating Python on GPUs
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02:44 MIN
Key milestones in the evolution of AI and GPU computing
AI Factories at Scale
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