NVIDIA's approach requires connecting thousands of Grok chips, creating latency bottlenecks. Cerebras's CEO argues its single, integrated wafer-scale system avoids this "interconnect tax," offering superior memory bandwidth and performance for massive models by eliminating the wiring between thousands of tiny chips.

AI startup Wafer has demonstrated that with proper software optimization, AMD chips can achieve 80-100% of Nvidia's performance (in tokens per second) for specific open-source models, at roughly half the cost. This challenges the notion of Nvidia's insurmountable hardware dominance by proving software is a key performance unlock.

FAL achieved order-of-magnitude speed improvements not just from optimizing hardware usage, but by post-training the AI model itself to be more compatible with their custom system kernels. This co-design approach shatters typical performance ceilings that rely on systems optimization alone.

OpenAI achieved a major reduction in the cost of running its models through purely software and algorithmic improvements, such as quantization and smarter caching. This demonstrates that efficiency innovation can be as impactful as acquiring more hardware, suggesting a path to overcoming compute bottlenecks without relying solely on expensive chips.

Instead of running an entire inference task on a single GPU, the next efficiency leap will come from breaking it down. Tasks like prefill, attention, and feed-forward networks will be routed to specialized chips, such as SRAM-based accelerators, that are best suited for each job, dramatically improving performance and ROI.

Model architecture decisions directly impact inference performance. AI company Zyphra pre-selects target hardware and then chooses model parameters—such as a hidden dimension with many powers of two—to align with how GPUs split up workloads, maximizing efficiency from day one.

The gap between a basic and a highly optimized inference setup is massive. Stacking techniques like quantization, custom speculative decoders, and KV-aware routing can yield performance improvements of 4x to 10x over a standard baseline for the same model and hardware.

Typically, first-gen custom silicon lags established players. OpenAI's 'Jalapeño' inference chip, however, is reportedly more efficient than Nvidia's next-gen Blackwell. This rapid success challenges the assumption that new chip development takes years to become competitive, signaling a major disruption.

While training has been the focus, user experience and revenue happen at inference. OpenAI's massive deal with chip startup Cerebrus is for faster inference, showing that response time is a critical competitive vector that determines if AI becomes utility infrastructure or remains a novelty.