AI Memory Wall Deepens as Compute Outruns HBM Bandwidth by 3x Every Two Years, Micron Warns

Micron warns AI memory wall is worsening

Micron Technology has reported that the demands of artificial intelligence (AI) are outpacing the capabilities of High Bandwidth Memory (HBM), with compute performance outstripping HBM bandwidth by three times every two years. This widening gap contributes to what is being referred to as the "memory wall," which is increasingly impacting the performance of AI systems. As AI large language models (LLMs) such as Meta's Llama 3 evolve, the need for advanced memory technologies is becoming critical. Micron highlighted that failures in HBM were responsible for 17% of training interruptions for Llama 3, underscoring the urgent need for further innovation in memory solutions to meet the growing demands of AI. Currently, HBM is the dominant DRAM type utilized in leading AI accelerators. However, as the performance of AI systems continues to improve, the limitations of existing memory solutions pose a significant challenge. Micron emphasizes the importance of advanced packaging and process technologies to address thermal constraints and enhance memory performance, which are essential to sustain the evolution of AI technologies.

Micron Says AI's Memory Wall is Worsening as Compute outruns HBM Bandwidth by 3x every Two Years, Advanced Packaging & Process To Tackle Rising Thermal Constraints

Micron explores what High Bandwidth Memory (HBM) is, why it is essential, how it compares to traditional DDR, and the key challenges that demand further innovation to sustain AI's growing demands. Micron Reveals HBM Failures Caused 17% of Meta's Llama 3 Training Interruptions as the Memory Wall Deepens As AI LLMs continue to evolve, the need for advanced memory technologies has become vital for the continuous scaling and improvement of these models. But memory is now becoming a major bottleneck that is limiting the performance of AI systems. HBM is currently the leading DRAM type used in leading AI accelerators. To attain maximum operations on a typical AI system, a large portion of the work is bound by memory as compute processors wait for more data to arrive. Once the work is complete, the system enters a compute-bound phase in which data is delivered to it fast enough to keep the processors busy. With HBM, the memory bandwidth ceiling is elevated, allowing memory-intensive AI workloads to achieve higher performance before hitting memory limits. Micron's HBM Design Architecture Fellow, Raghu Sreeramaneni, shed light on this "Memory Well" during its Hot Chips 2026 presentation. The company states that while compute capabilities are advancing at a rate of 3x every two years, memory is only advancing at <2x every two years. Currently, there are three types of memory architectures: 2.5D attached memory, 2.5D advanced memory, and Processing-in-memory DRAM. The key takeaways of the Hot Chips 2026 talk include: * Memory is central to scaling LLM performance (more compute, larger datasets, more parameters). * Compute scales ~3× every two years; HBM bandwidth scales only ~2× every two years → the memory wall persists and may be worsening. * In a typical GPU SIP with four 12-high HBM stacks, memory silicon accounts for ~90% of the total silicon (8× the GPU silicon). * HBM is the most cross-functionally complex memory solution (packaging + process + design) in a form factor the size of a postage stamp. * AI workloads are often memory-bound; higher HBM bandwidth shifts the roofline upward and unlocks more of the processor's peak performance. * Stack heights have grown from 4-high to 16-high (path to 20-high exists but faces major thermal/mechanical hurdles). * Meta's Llama 3 training paper attributed 17% of unintended interruptions to HBM. This accelerated pace in AI requires an advanced memory solution, and HBM has a vital role to play in this segment. The problem with existing HBM is that as density increases per DRAM, the efficiency (pJ/bit) goes down. To address this, the industry needs to move to disruptive process & packaging technologies, and the capability to offload from the GPU through advanced D2D PHY & have the ability to support custom features. Currently, Micron is offering HBM4, which is its fastest HBM solution to date. It offers up to 2800 GB/s bandwidth, twice the IO, and has double the channels of HBM3E. Each generation, HBM is seeing an increase in data rate, Pesudo-Channels, density, and Stack Height. The basic building blocks of HBM remain the same, with incremental changes each generation. HBM architectures include stacked DRAM dies that contain multiple independent channels & each channel has 2 pseudo-channels, which include a shared Command/Address bus and an independent data bus. Then there's the base die, which interfaces command and data between the host and DRAM. The base die has a Microbump PHY that connects the host to the base die, and features a 3D TSV PHY that interconnects the DRAM stack with the base die. This spine runs from the top all the way to the bottom of the HBM cube. The HBM Cube is then connected to the interposer using highly dense and short point-to-point connections (HBM4 features 2K IOs). Coming to the advantages of HBM over traditional DDR-based DRAM solutions, the first is the much higher system bandwidth that can be achieved on a GPU using multiple HBM stacks. While DDR-based solutions offer higher capacities, they also take up more space, whereas HBM sits closer to the GPU and offers bandwidth in the multi TB/s region, versus multi GB/s of DDR. HBM vs. Traditional DDR DRAM There are also disadvantages that come with HBM. One of these is the thermal problem that becomes a bigger issue as the solution scales. Micron calls for the need for thermal innovations to address this challenge. The problem is not that much of a power problem but more of a power density problem, and the base die is the region that is vastly affected due to most of the advanced functionality lying within the base die, and as stack heights increase, that leads to increased die activity. Some solutions include Liquid cooling and Hybrid bonding, but Micron is also working on next-gen "advanced packaging" solutions that can help mitigate the thermal problems. These include the use of Fusion bonding to support extremely tight pitches with single-digit micron resolution, allowing the movement of significantly more data through the package. It reduces the number of dielectric layers between the DRAM dies within the stack, improving the thermal resistance. Micron is also looking into new high-speed IO designs with a more memory-optimized SerDes solution. There is also research going on in expanding HBM stacks beyond 16-Hi, but Micron states there's still a lot of work that needs to be done. HBM has emerged as the indispensable foundation powering today's AI systems -- delivering extraordinary bandwidth, density, and energy efficiency through sophisticated stacking, extreme parallelism, and advanced packaging that traditional DDR simply cannot match. Yet the path forward is far from straightforward. The persistent memory wall, the complex interplay of thermals, reliability, chip-package interactions, and ever-taller stacks demand continuous, cross-functional innovation. As AI workloads evolve and the boundary between compute and memory continues to blur, the industry's collective expertise will be essential to overcome these challenges, scale capacity and performance, and sustain the remarkable progress that is reshaping the future of computing. Follow Wccftech on Google to get more of our news coverage in your feeds.