AI Inference Needs A Mix-And-Match Memory Strategy

AI inference workloads vary widely in terms of latency, bandwidth, capacity, and compute requirements, necessitating a mix-and-match memory strategy to optimize cost efficiency. Different types of AI workloads, such as interactive LLMs, long-context reasoning, ranking models, and batch inference, stress hardware in distinct ways. The dual-stage nature of inference, involving prefill and decode processes, calls for tailored memory solutions like GDDR for prefill and HBM for decode stages. Leading vendors like NVIDIA are adopting disaggregated memory architectures to enhance inference efficiency and reduce costs. Qualcomm is also exploring LPDDR for disaggregated inference to balance capacity, utilization, and cost effectively.