AI Inference Compute: The Next Frontier for Specialized Hardware
While the immense computational demands of training artificial intelligence models often dominate headlines and captivate investors, a quieter yet equally profound challenge is emerging: the requirements of AI inference. This phase, where trained AI models are actually put to use, is rapidly evolving and may soon push today’s most advanced GPUs to their limits.
Sid Sheth, founder and CEO of d-Matrix, points out a significant shift in the AI landscape. The world of AI model training has historically been “monolithic,” largely dominated by GPUs, particularly those from a single prominent company. However, the realm of AI inference presents a stark contrast. It is far from a one-size-fits-all scenario, characterized by an extensive variety of workloads, each demanding distinct computational requirements. Some users prioritize cost-efficiency, others seek real-time interactivity with the model, while a third group might be solely focused on maximizing data throughput. This inherent diversity means that no single hardware architecture or computing infrastructure can efficiently serve all these varied needs simultaneously. Sheth anticipates a truly “heterogeneous” future for inference, where specialized, best-in-class hardware will be deployed to meet the specific demands of individual users and applications.
One of the most critical technical hurdles in AI inference is ensuring that memory, which stores the data, remains as physically close as possible to the compute units that process it. This proximity is vital because AI workloads, especially those involving generative AI, necessitate frequent access to memory. When generating content, models rely heavily on caching previous data. Every new “token” — a piece of data like a word or sub-word — generated requires tapping into this cached information to determine the next optimal output. This problem intensifies dramatically with AI agents, escalating memory demands by tenfold or even a hundredfold. Consequently, minimizing the distance data must travel between memory and compute becomes paramount, directly impacting the speed, efficiency, and cost-effectiveness of inference operations.
Companies are actively innovating to address this challenge. For instance, d-Matrix’s Corsair AI inference platform exemplifies a novel approach to architecting and locating memory and compute. The company builds specialized chiplets, which are then co-packaged into a flexible fabric. This design provides the platform with critical elasticity and modularity, allowing it to scale up or down precisely according to customer requirements. Within Corsair, memory and compute layers are stacked directly atop each other, akin to a stack of pancakes. This revolutionary design drastically reduces the physical distance data needs to travel. As Sheth describes it, data effectively “rains down” from the memory into the compute units directly beneath it, with the increased surface area between the layers facilitating a much higher volume of data transfer.
As AI applications continue to proliferate and mature, the spotlight is gradually shifting from the initial heavy lifting of model training to the ongoing, diverse, and equally demanding task of running them at scale. The future of AI infrastructure will undoubtedly be shaped by these evolving inference requirements, driving a new wave of specialized hardware innovation.