Redefining AI

Performance AI

Data Centers Built for AI

Pascaline designs data centers from the chip-out to deliver tailored, scalable, eco-conscious, and highly efficient data centers that meet the unique needs of AI.

Learn more - www.pascaline.ai

A New Design for Data Centers - Localizing data storage

At present, data centers are designed from the outside-in. Data gets sent from outside sources to inner frames (e.g. the Cloud as example). By augmenting GPU use to that of a GPU processor, we can effectively shift the workload from the GPU to the data center itself.

AI Workloads and Data Movement

The exponential rise in AI workloads puts significant strain on data movement within AI systems. As data volumes grow, the energy and power consumed in moving data to meet model queries escalate. Localizing data storage offers a solution to mitigate these rising costs.

Reframing Data Metrics: Cost of Inference

1000x More Accurate, 1/1000th the Cost

Cost of Inference is the operational costs of running a trained AI model. Composed of computational resources, memory usage, energy consumption, and processing time for new data inputs, Cost of Inference is often projected to far exceed AI model training costs.

Accuracy Reduces Cost of Inference

Smaller, highly accurate AI models require less correction and additional processing to account for errors, drastically lowering output costs.

CAT: 1000x More Accurate, 1/1000th the Cost

AI models today are shaped by statistical frameworks that overlook subtle connections among data points, yielding results based on probabilities rather than precise predictions. These models often grow in complexity and size to accommodate missing information, sacrificing precision along the way.

In contrast, Category Theory (CAT) provides a clear framework for defining relationships between data points, minimizing guesswork and the need for corrections. AI models structured with CAT are not only more precise and accurate, but also smaller.

CAT-based models are anticipated to slash inference costs by up to 1000 times, while achieving accuracy improvements ranging from 1000 times to as much as 1 million times.

Redefining AI: Design and Focus

Performance AI

Data Centers Built for AI

A New Design for Data Centers - Localizing data storage

AI Workloads and Data Movement

Reframing Data Metrics: Cost of Inference

1000x More Accurate, 1/1000th the Cost

Accuracy Reduces Cost of Inference

CAT: 1000x More Accurate, 1/1000th the Cost

Learn more about Data Centers at Pascaline.AI

Redefining AI: Design and Focus

Performance AI

Data Centers Built for AI

A New Design for Data Centers - Localizing data storage

AI Workloads and Data Movement

Reframing Data Metrics: Cost of Inference

1000x More Accurate, 1/1000th the Cost

Accuracy Reduces Cost of Inference

CAT: 1000x More Accurate, 1/1000th the Cost

Learn more about Data Centers at Pascaline.AI

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