AI Inference Workloads: Overcoming Challenges of taking AI to Production

AI and MLOps engineers in federal agencies often struggle to deploy models on GPUs. Most Al research initiatives never make it to production. Why? Researchers are facing bottlenecks due to static allocations of GPUs, and different technology sets complicate moving models from training to production.

View this on-demand webinar from Run.AI and Carahsoft to learn Carahsoft to learn how your agency can overcome the challenges associated with new hardware-accelerated AI modeling practices and discover how traditional best practices have evolved to become more efficient

During this on-demand session, you will learn from our experts how to:

Run multiple inference workloads on the same GPU by using the concept of fractional GPUs
Remove the bottlenecks which prevent almost 80% of workflows from reaching production
Get dynamic MIG slices for each new job when using the NVIDIA A100 GPU
Improve GPU utilization when running inference workloads
Maintain high throughput and low latency for model serving