Enterprise AI infrastructure has quietly stopped being a single-hardware decision. For years, the default answer to "what do we run this on?" was simply "GPUs." But as organizations move AI from pilot projects into production workloads that run continuously, at scale, and under real cost and power constraints, that answer is changing.
TL;DR
Heterogeneous AI infrastructure combines GPUs, and purpose-built AI accelerators in one system, matching each AI workload stage to its ideal hardware.
No single chip is optimal for every stage. Training, prefill, and decode each demand different compute and memory bandwidth.
In heterogeneous inference, GPUs handle compute-bound prefill while purpose-built accelerators like SambaNova's Reconfigurable Dataflow Units (RDUs) handle memory-bound decode.
