Supersizing AI: The Changing Role of Advanced Packaging

October 22, 2026

Among these shifts, inference provides a clear example of how changing workload economics are leading to greater architectural diversity. The token economy is forcing system architects to optimize not only for raw performance but also for the cost and energy consumed per token generated. In many cases, the primary competitive metric is no longer training time, but rather how efficiently an AI platform can deliver inference capacity at hyperscale.

How AI inference is expanding the range of viable packaging architectures

While training systems still rely heavily on HBM capacity and bandwidth, many inference workloads prioritize low latency, high token throughput, and efficiency over maximum memory bandwidth. This difference creates opportunities to optimize AI systems differently at both the package and system levels.

A good example is NVIDIA's decision to integrate technology from Groq into its next-generation inference ecosystem, highlighting the growing attractiveness of HBM-less architectures for pure inference acceleration. By eliminating dependence on large HBM banks and using monolithic silicon with on-board SRAM, these devices can achieve competitive inference performance without requiring a complex 2.5D integration strategy.

Removing HBM also reduces package complexity, lowers manufacturing cost, simplifies power delivery, and allows for more conventional packaging approaches. At the system level, it also places greater emphasis on networking infrastructure, as disaggregated memory and distributed inference become increasingly important sources of scale.

Why advanced packaging is becoming an architectural decision

The advanced packaging market is growing and moving toward a more mixed and integration-heavy set of package architectures. As shown above, different ways of handling compute and memory can require different packaging. For suppliers, this means it's more important than ever to understand how these strategies are evolving and what packaging they need.

TechInsights forecasts datacenter compute processor package shipments will rise from 45.2 million units in 2025 to 102.7 million units in 2030, representing a compound annual growth rate of approximately 15%. The market is expanding as its technical requirements are becoming less uniform.

The full TechInsights analysis looks at other forces contributing to this diversification. We explore how packaging is adapting to meet the demands of advanced memory systems, take a closer look at bridge-based approaches such as CoWoS and EMIB, explore the growing disconnect between packaging innovation and predetermined scaling roadmaps, and show how these packaging strategies are performing in market forecasts.