OpenAI–Cerebras Deal Signals Selective Inference Optimization, Not Replacement of GPUs

OpenAI’s partnership with Cerebras has raised questions about the future of GPUs in inference workloads. Cerebras uses a wafer-scale architecture that places an entire cluster onto a single silicon chip. This design reduces communication overhead and is built to improve latency and throughput for large-scale inference.

Mark Jackson, Senior Product Manager at QumulusAI, says Cerebras’ architecture is best suited for narrowly defined, high-demand inference environments where extremely large request volumes require low latency and strong throughput. He maintains that GPUs remain the practical default for most organizations because they support training, experimentation, fine-tuning, and inference within a mature ecosystem.

He adds that fully replacing GPUs with specialized silicon would introduce additional operational complexity without broad justification. Jackson views the development as a move toward more diversified AI infrastructure, where GPUs remain foundational and targeted accelerators are deployed only when they deliver clear performance or economic advantages.