AI and deep learning inference demand powerful AI accelerators, but are you truly maximizing yours?
GPUs often operate at a mere 30-40% utilization, squandering valuable silicon, budget, and energy.
In this session, NeuReality's Field CTO, Iddo Kadim, tackles the critical challenge of maximizing AI accelerator capability. Whether you build, borrow, or buy AI acceleration – this video is a must watch.
Iddo will reveal a multi-faceted approach encompassing intelligent software, optimized APIs, and efficient AI inference instructions to unlock benchmark-shattering performance for ANY AI accelerator.
The result?
You’ll get more from the GPUs buy, rather than buying more GPUs to make up for the limitations of today’s CPU and NIC-reliant inference architectures. And, you’ll likely achieve superior system performance within your current energy and cost constraints.
Your key takeaways:
- The urgency of GPU optimization: Is mediocre utilization hindering your AI initiatives? Discover new approaches to achieve 100% utilization with superior performance per dollar and per watt leading to greater energy efficiency.
- Factors impacting utilization: Master the key metrics that influence GPU utilization: compute usage, memory usage, and memory bandwidth.
- Beyond hardware: Harness the power of intelligent software and APIs. Optimize AI data pre-processing, compute graphs, and workload routing to maximize your AI accelerator (XPU, ASIC, FPGA) investments.
- Smart options to explore: Uncover the root causes of underutilized AI accelerators and explore modern solutions to remedy them. You’ll get a summary of recent LLM real-world performance results – made possible by pairing NeuReality’s NR1 server-on-a-chip with any GPU or AI accelerator.
You spent a fortune on your GPUs – don’t let them sit idle for any amount of time.
