Source: SiliconANGLE
As AI inference shifts from latency-obsessed benchmarks to real-world production workloads, token generation speed—not raw throughput—has emerged as the actual constraint. This changes the economics of serving LLMs at scale: when you're bottlenecked by sequential token output rather than batch processing, hardware vendors, cloud providers, and model optimizers are architecting different solutions (specialized silicon, attention mechanisms, KV cache strategies). The winner won't be whoever builds the fastest GPU, but whoever solves sustained token generation across heterogeneous hardware setups.