The Evolving Landscape of AI Accelerators and High Bandwidth Memory

Introduction: The Accelerating Pace of AI Compute

The artificial intelligence landscape is evolving at an unprecedented rate, driven by increasingly complex models and the insatiable demand for computational power. At the heart of this evolution lie AI accelerators, specialized processors designed to handle the massive parallel computations required for training and inference. High Bandwidth Memory (HBM) has emerged as a critical component, acting as the lifeblood that feeds these accelerators, enabling them to reach their full potential. This report, drawing insights from industry analysis, explores the symbiotic relationship between AI accelerators and HBM, dissecting the current market, emerging trends, and the strategic implications for key players.

The SemiAnalysis AI Accelerator Model: A Comprehensive View

SemiAnalysis offers a robust AI accelerator model that provides a granular look at historical and projected accelerator production across various companies and product types. This model is an invaluable tool for hyperscalers, semiconductor manufacturers, and investors seeking to understand competitive positioning, supply chain intricacies, and future market trajectories. It extends its analysis to upstream and downstream supply chain elements, from equipment requirements to deployed capacity and computational performance (FLOPS). The data, provided on a quarterly basis from 2023 to 2027, includes shipment volumes, Average Selling Prices (ASPs) for a wide array of AI accelerators from major vendors such as Nvidia, Google, Meta, AWS, Microsoft, Apple, AMD, and Intel, among others. This detailed forecasting allows for revenue estimations across the supply chain, impacting everything from wafer fabrication to final deployment.

High Bandwidth Memory (HBM): The Memory Wall Crusher

The relentless growth in AI model size and complexity has created a significant challenge known as the "memory wall." Traditional memory solutions struggle to keep pace with the data demands of modern AI workloads, leading to performance bottlenecks. HBM has become the de facto solution, offering a unique combination of high speed, power efficiency, and area efficiency. Its vertically stacked DRAM dies, connected via Through-Silicon Vias (TSVs), provide a significantly wider data bus compared to conventional DDR memory, enabling terabytes per second of bandwidth. This is crucial for both training, which is often bandwidth-constrained, and inference, where handling longer context windows and larger key-value (KV) caches is paramount. The increasing proportion of HBM in the Bill of Materials (BOM) for AI accelerators underscores its critical importance.

HBM Technology: Advancements and Vendor Dynamics

The HBM market is characterized by intense competition and continuous innovation. SK Hynix has established a dominant position, largely attributed to its proprietary Mass Reflow Molded Underfill (MR-MUF) technology, which offers superior thermal performance and higher yield rates compared to the Thermal Compression with Non-Conductive Film (TC-NCF) approach favored by competitors like Samsung and Micron. This technological edge has positioned SK Hynix as a primary supplier for leading AI accelerator manufacturers, particularly Nvidia. However, the industry is on the cusp of significant shifts with the advent of HBM4. This next-generation memory promises enhanced bandwidth and efficiency, with a key innovation being the potential for a customized base die. This allows for tailored logic and accelerator circuitry, offering performance advantages and enabling custom HBM variants for specific customer needs.

The Rise of Custom HBM and its Implications

The trend towards custom HBM, particularly with HBM4, is a pivotal development. Companies like Nvidia and AMD are reportedly working on custom base die implementations to gain a competitive edge. This customization moves beyond off-the-shelf solutions, allowing for optimized logic that can enhance data routing efficiency, reduce latency, and improve overall performance, especially in inference scenarios where latency is critical. While ASIC designers may adopt custom base dies by the HBM4E timeframe (around 2027), Nvidia and AMD are expected to have custom HBM4 solutions ready for their 2026 products. This strategic move by major players is expected to push third-party ASIC solutions further ahead and necessitates a redoubling of efforts by competitors to keep pace.

Disaggregated Serving: Optimizing Inference with Specialized Hardware

The inference process in large language models (LLMs) can be broadly divided into two phases: prefill and decode. The prefill phase is compute-intensive, generating the initial token from a prompt, while the decode phase generates subsequent tokens, heavily relying on memory bandwidth to access the KV cache. Recognizing this dichotomy, Nvidia has introduced specialized hardware like the Rubin CPX GPU. Designed for the prefill phase, the Rubin CPX emphasizes compute FLOPS over memory bandwidth, utilizing less expensive GDDR7 memory instead of HBM. This disaggregated approach, where specialized hardware caters to specific phases of inference, promises significant cost savings and performance improvements. By offloading prefill tasks to specialized, more cost-effective chips, the utilization of expensive HBM is optimized for the memory-bandwidth-bound decode phase.

The Rubin CPX and Rack-Scale Innovations

Nvidia