Separating inference into "prefill" (memory-bound) and "decode" (bandwidth-bound) tasks is a game-changer for hardware longevity. It allows older GPUs to be used for prefill tasks indefinitely, extending their useful economic life from 3-4 years to 10-15 years, a boon for data centers and their financiers.

AI workloads are limited by memory bandwidth, not capacity. While commodity DRAM offers more bits per wafer, its bandwidth is over an order of magnitude lower than specialized HBM. This speed difference would starve the GPU's compute cores, making the extra capacity useless and creating a massive performance bottleneck.

Agentic AI has a different computational profile than previous generative AI. It uses much larger inputs with high reusability, creating larger KV caches. This change makes GPU architectures, which were optimized for earlier workloads, inefficient for the new demands of agentic inference.

The high cost of GPUs means any inefficiency during model training is extremely expensive. This economic reality justifies building specialized, AI-focused infrastructure with features like advanced observability and optimized storage to maximize GPU utilization and prevent costly delays from failures or slowdowns.

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.

While training AI models is a compute-bound problem where more flops yield better results, inference (running the model) is memory-bound. Each token generation requires reading all model weights from memory, making memory bandwidth, not raw processing power, the primary performance bottleneck.

The key advantage of larger GPU clusters is their ability to use the memory bandwidth of all GPUs in parallel to load model weights. This massive aggregate bandwidth dramatically reduces memory fetch times, which is a primary latency bottleneck, especially for very large, sparse models.

Standard inference tooling is designed for one large model on many GPUs. Efficiently serving multiple small models requires the opposite architecture: packing many models onto a single GPU with fast switching to avoid paying for idle hardware, a fundamentally different infrastructure problem.

GPUs are ill-suited for generating output tokens because they process models in discrete chunks called "kernels." This method requires constant data transfer to and from external memory, creating a significant bottleneck limited by memory bandwidth, which ultimately slows down real-time inference.