Massive models exceeded server memory. An AI factory now powers agents that write code and turn design flowcharts into specs.
A global semiconductor leader enabling nearly two billion connected devices annually with systems-on-chips for mobile, home, and IoT markets.
Developing large language models required processing billions of tokens monthly, but models with hundreds of billions of parameters exceeded the...
“Our AI factory, powered by DGX SuperPOD, processes approximately 60 billion tokens per month for inference and completes thousands of model-training iterations every month.”
Fabless semiconductor designer of chipsets for mobile, home, and automotive devices.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
MediaTek's Chip design automation is part of this use case:
Related implementations across industries and use cases
QC simulations ran 15 hours and reset from scratch on every change. Digital twins cut that to 3.6 seconds.
Developers waited 15 minutes for routine fixes. Now, they run parallel agents to finish tasks in 30 seconds.
Leaning on external APIs for sensitive government workflows cost MISA control and cash. On-premise AI factory cut it.
Leaning on external APIs for sensitive government workflows cost MISA control and cash. On-premise AI factory cut it.
Processing 2T tokens on CPUs took days. GPU acceleration cut prep to hours, unlocking a 5% accuracy gain.
Manual workflows delayed global campaigns for weeks. Now, local teams use GenAI to instantly draft localized, regulatory-compliant copy.
Manually updated profiles left skills stale. The first prototype took two hours per 1,000 consultants—now the agent covers 130K in minutes.
Scattered AI tools and manual document searches slowed engineers. Now, a unified AI rapidly retrieves specialized technical answers.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
Massive models exceeded server memory. An AI factory now powers agents that write code and turn design flowcharts into specs.
A global semiconductor leader enabling nearly two billion connected devices annually with systems-on-chips for mobile, home, and IoT markets.
Developing large language models required processing billions of tokens monthly, but models with hundreds of billions of parameters exceeded the...
“Our AI factory, powered by DGX SuperPOD, processes approximately 60 billion tokens per month for inference and completes thousands of model-training iterations every month.”
Fabless semiconductor designer of chipsets for mobile, home, and automotive devices.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
MediaTek's Chip design automation is part of this use case:
Related implementations across industries and use cases
QC simulations ran 15 hours and reset from scratch on every change. Digital twins cut that to 3.6 seconds.
Developers waited 15 minutes for routine fixes. Now, they run parallel agents to finish tasks in 30 seconds.
Leaning on external APIs for sensitive government workflows cost MISA control and cash. On-premise AI factory cut it.
Leaning on external APIs for sensitive government workflows cost MISA control and cash. On-premise AI factory cut it.
Processing 2T tokens on CPUs took days. GPU acceleration cut prep to hours, unlocking a 5% accuracy gain.
Manual workflows delayed global campaigns for weeks. Now, local teams use GenAI to instantly draft localized, regulatory-compliant copy.
Manually updated profiles left skills stale. The first prototype took two hours per 1,000 consultants—now the agent covers 130K in minutes.
Scattered AI tools and manual document searches slowed engineers. Now, a unified AI rapidly retrieves specialized technical answers.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.