Compute | New York // Major Panel: Reading between the lines: Striking the XPU/GPU/CPU balance to adapt to AI inference workloads?

GenAI is growing 800x every 2 years, but compute is growing at a rate of less than 2x and a GPU supply shortage looms, but is the rush for GPUs going to continue as AI inference becomes the dominant workload? Could CPUs act as cost-effecitve alternative?

Or will inference leave GPUs gathering dust?

How should you balance hardware performance in the short and long-term? Join our talk to find out:

• Primrose Path: As inferencing makes up 85% of the AI compute problem, is the focus on GPUs over CPUs leading everyone down the wrong path?
• Getting Lean: How will changes in model building from going bigger to higher efficiency models affect the choice of compute hardware?
• GP4U: How should you think about the rise of GPU-as-a-Service and its role in next gen AI workloads?