In brief: Data-intensive applications such as artificial intelligence, high-performance computing, high-end graphics, and servers are increasingly eating up high-bandwidth memory. Just in time, the JEDEC Association has published preliminary specifications for the 4th-generation standard. It offers substantial performance gains over its predecessor, including higher bandwidth, increased capacity, improved speed and a doubled channel count.
Last week, the JEDEC Association published preliminary specifications for the 4th-generation high-bandwidth memory. Once the HBM4 standard is finalized, it will represent a major advancement in memory technology for high-performance computing applications. Just as significantly, the new standard will set the direction for future memory technologies and ensure interoperability across different manufacturers.
HBM4 is designed to further enhance data processing rates, offering higher bandwidth and increased capacity per die and/or stack compared to its predecessor, HBM3. It also aims to maintain lower power consumption, which is crucial for large-scale computing operations.
Technical advancements include a doubled channel count per stack compared to HBM3, a larger physical footprint, compatibility with HBM3 through a single controller, and support for 24 Gb and 32 Gb layers. There is also an initial agreement on speed bins up to 6.4 Gbps, with discussions about higher frequencies. Missing from the specs is the integration of HBM4 memory directly on processors, which Tom's Hardware says is perhaps the most intriguing part about the new type of memory.
HBM4 is particularly important for generative artificial intelligence, high-performance computing, high-end graphics cards, and servers. In particular, AI applications will benefit from the data processing and memory capabilities the standard will offer, allowing AI applications to handle larger datasets and perform complex calculations more quickly.
The higher bandwidth in HBM4 will enable AI models to process information more efficiently, leading to faster training and inference times. In addition, HBM4's focus on lower power consumption is essential for large-scale AI operations. As a result, a more sustainable and cost-effective deployment of AI systems, particularly in data centers and high-performance computing environments, will be possible.
Also, the larger memory capacities per die and stack will enable AI applications to work with more extensive datasets and more complex models without the need for frequent data transfers between memory and processing units. Finally, the enhancements also ensure that the interposer can handle higher data rates and signal density, resulting in better overall system performance for AI applications.
Something else to note is because HBM4 is designed to be compatible with HBM3 through a single controller, it will facilitate easier adoption and integration into existing AI hardware infrastructures. Furthermore, because HBM4 offers options for 4-high, 8-high, 12-high, and 16-high TSV (Through-Silicon Via) stacks, there will be greater flexibility in memory configuration to meet the specific needs of different AI applications.