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World Congress 2026 Europe • Jul 9, 2026 • Session details

RTX AI PC: Developing local and edge AI applications

Joerg Krall

For under a dollar, you can now fine-tune personalized AI models locally. Discover how RTX hardware eliminates cloud dependencies to power frictionless, zero-latency edge applications.

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#1 about 3 min

Balancing local developer needs and enterprise IT constraints

Local developers require direct deployment access without restrictive data center interference.

#2 about 5 min

NVIDIA local and edge AI hardware capabilities overview

An overview of consumer workstations and dedicated nodes highlights different memory performance limits.

#3 about 2 min

Introduction to Black Forest Labs generative models

Open-weight architectures provide scalable foundational image generation capabilities for production platforms.

#4 about 4 min

Running generative models locally on consumer RTX hardware

Compact distilled variations enable rapid local image synthesis and modification tasks directly on consumer graphical processors.

#5 about 2 min

Cost-effective LoRA fine-tuning workflows on local inference hardware

Training targeted style representations locally on graphical processors drastically reduces ongoing operational costs.

#6 about 5 min

Live text-to-image generation and image editing implementation

Local execution pipelines efficiently output high-fidelity text-to-image assets via interactive cloud workspaces.

#7 about 3 min

Generating consistent sprite sheets for video character design

Locally trained representations can accurately synthesize multi-directional assets specifically for complex graphical game projects.

#8 about 2 min

Near real-time visual style transfers on webcam streams

Live stylistic visual transformations process smoothly across active webcam feeds utilizing optimized local architectures.

#9 about 3 min

Development roadmap for world models and physical robotics

Emerging simulation frameworks will eventually guide and instruct safe autonomous physical machines through actionable real-world data.

#10 about 2 min

Minimum hardware investments for local AI project workflows

Establishing efficient initial setups involves balancing dedicated on-premise hardware against flexible cloud GPU rental strategies.

Matching moments

54 sec

Running generative AI models in local environments

Cedric Clyburn Cedric Clyburn · WWC 2024

2:26 min

Cost and latency pressures pushing AI to the edge

Moe Sani Moe Sani · WWC Europe 2026

3:21 min

Reducing cloud dependency with on-device edge AI models

Precious Osaro Precious Osaro · WWC Europe 2026

2:12 min

Distilling cloud AI capabilities into local device models

2:54 min

Leveraging Chrome AI and nano models for web applications

Raymond Camden · Perf + AI

3:19 min

Technological shifts enabling practical edge AI deployment

Sasha Denisov Sasha Denisov · WWC Europe 2026

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