Precompiling hardware-specific AI models took weeks per update. On-device generation enabled lightweight, single-package deployments.
A provider of photo and video enhancement tools serves a user base operating millions of PCs with vastly different hardware specifications across multiple GPU generations.
Delivering consistent AI performance required the team to pre-generate and ship thousands of large, static inference engines for different GPU types....
“Our team’s successful integration of TensorRT for RTX demonstrates the library’s effectiveness in providing substantial inference acceleration with minimal integration efforts. This collaboration not only enhances the current capabilities of Topaz Video but also paves the way for exciting future developments. We sincerely appreciate NVIDIA’s efforts in this direction of delivering performance with portable inference with libraries like TensorRT for RTX.”
AI-powered photo and video editing software for professionals and enterprise.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Related implementations across industries and use cases
Cloud negotiations took weeks while usage tripled. Migrating to optimized H100s cut costs 60% and sped up inference 3x.
Buying hardware for 10x overnight spikes was risky. On-demand GPUs now absorb volatility, enabling same-day scaling.
Camera-shy executives and scheduling conflicts bottlenecked output. Avatars now generate video on demand without physical shoots.
Buying hardware for 10x overnight spikes was risky. On-demand GPUs now absorb volatility, enabling same-day scaling.
Engineers spent weeks manually configuring infrastructure. Now, they deploy pre-optimized models in minutes.
Scattered spreadsheets couldn't catch AI hallucinations. Now, automated LLM judges evaluate every prompt change to block regressions.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Hundreds of pages per board book slowed director prep. Now, isolated AI securely condenses sensitive materials into actionable briefs.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.
Precompiling hardware-specific AI models took weeks per update. On-device generation enabled lightweight, single-package deployments.
A provider of photo and video enhancement tools serves a user base operating millions of PCs with vastly different hardware specifications across multiple GPU generations.
Delivering consistent AI performance required the team to pre-generate and ship thousands of large, static inference engines for different GPU types....
“Our team’s successful integration of TensorRT for RTX demonstrates the library’s effectiveness in providing substantial inference acceleration with minimal integration efforts. This collaboration not only enhances the current capabilities of Topaz Video but also paves the way for exciting future developments. We sincerely appreciate NVIDIA’s efforts in this direction of delivering performance with portable inference with libraries like TensorRT for RTX.”
AI-powered photo and video editing software for professionals and enterprise.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Related implementations across industries and use cases
Cloud negotiations took weeks while usage tripled. Migrating to optimized H100s cut costs 60% and sped up inference 3x.
Buying hardware for 10x overnight spikes was risky. On-demand GPUs now absorb volatility, enabling same-day scaling.
Camera-shy executives and scheduling conflicts bottlenecked output. Avatars now generate video on demand without physical shoots.
Buying hardware for 10x overnight spikes was risky. On-demand GPUs now absorb volatility, enabling same-day scaling.
Engineers spent weeks manually configuring infrastructure. Now, they deploy pre-optimized models in minutes.
Scattered spreadsheets couldn't catch AI hallucinations. Now, automated LLM judges evaluate every prompt change to block regressions.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Hundreds of pages per board book slowed director prep. Now, isolated AI securely condenses sensitive materials into actionable briefs.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.