Accelerated computing offloads parallelizable tasks from the CPU to specialized GPU cores, executing them simultaneously for a massive speedup.
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.
NVIDIA provides domain-specific libraries like cuDF that allow developers to accelerate their code, such as pandas dataframes, with minimal changes.
Modern AI development replaces complex, hard-coded logic with prompts to large language models, changing how developers implement functions like sentiment analysis.
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.
NVIDIA NIM provides enterprise-grade microservices for deploying AI models with features like runtime optimization, stable APIs, and Kubernetes integration.
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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32:27Christian Liebel
22:18Paul Graham
29:50Ekaterina Sirazitdinova
30:46Stephan Gillich, Tomislav Tipurić, Christian Wiebus & Alan Southall
24:26Thomas Schmidt
Jobs that call for the skills explored in this talk.
Deutsches Medizinrechenzentrum GmbH
