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.

Simply "scaling up" (adding more GPUs to one model instance) hits a performance ceiling due to hardware and algorithmic limits. True large-scale inference requires "scaling out" (duplicating instances), creating a new systems problem of managing and optimizing across a distributed fleet.

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.

RL models can be inefficient during inference. The GPU often sits idle while the CPU calculates rewards, then suddenly gets hit with a massive "burst" of activity. This unpredictable demand makes serving these models costly and complex, requiring conservative GPU allocation.

Unable to afford the industry-standard 'one GPU, one model' setup, Featherless AI was forced to develop a novel 'hot-swapping' inference platform. This technology, born from financial constraints, became their key competitive advantage, allowing them to serve thousands of models efficiently and affordably.

The intense power demands of AI inference will push data centers to adopt the "heterogeneous compute" model from mobile phones. Instead of a single GPU architecture, data centers will use disaggregated, specialized chips for different tasks to maximize power efficiency, creating a post-GPU era.

Inference engineering is not a monolith. Data center teams focus on making models "less slow" for massive throughput. Local AI teams focus on making models "less dumb" on constrained hardware, using methods like advanced quantization to fit models in memory.

A fundamental constraint today is that the model architecture used for training must be the same as the one used for inference. Future breakthroughs could come from lifting this constraint. This would allow for specialized models: one optimized for compute-intensive training and another for memory-intensive serving.

While the idea of distributed compute pools is appealing, it's not feasible for AI training due to high latency demands; GPUs must be physically co-located. However, AI inference is less sensitive to this lag, making a distributed network of compute (like home GPUs) a much more viable and exciting model.