LLM Generative AI

Generative AI requires LLMs as foundation models. LLMs need lots of GPU compute resources on-demand for training, fine tuning and inference. Manual allocation and managing resources leads to GPU availability, over provisioning and misconfigurations in infrastructure resulting in constant errors like OOMs and delays and disruptions in model runs. In addition, Data Scientists wait for valuable GPU compute resources to run the models. Rapt AI's compute platform for LLMs analyzes LLM model workloads, predict resources, optimizes compute resource and schedules the LLM models across any cloud and on-premise.

AI Health Care

To generate synthetic medical images, GANs are used in healthcare. GANs need lots of GPU resources to run multiple generators in parallel to generate these synthetic images. This leads to GPU availability issues, GPU under utilization and overprovisioning and Data Scientists waiting for GPU resources.

Rapt AI's intelligent computing platform enables Dynamic GPU share allocations, multiple generators in same GPU and save costs. All this with no human intervention. Just run your GANs without worrying about infrastructure.

5G

The ability to provide Al-on-5G is dependent on having the optimized accelerated compute (like GPU) at the edge. GPUs can be deployed at MEC in edge deployments for 5G. But the challenge at MEC is limited GPU compute leading slow processing of 5G AI applications at MEC.

Rapt AI's edge computing provides auto sharing of GPU resources at MEC dynamically based on the application workload. This leads to many applications to run faster with more compute power right at MEC.