Samsung Electronics has revealed a new generation of memory technology built for an AI industry that is quickly running into limits around speed, storage capacity and energy use.
The South Korean chipmaker used the Future of Memory and Storage 2026 conference in Santa Clara, California, to present several new technologies. The line-up included zHBM, zNAND-O and a prototype V10 Bonding V-NAND chip containing more than 400 layers.
Some of the products remain concept designs rather than chips ready for large-scale deployment. Still, Samsung is making its direction fairly clear. Memory can no longer sit quietly beside the processor while AI models demand faster access to enormous amounts of data.
It needs to become part of the computing architecture itself.
Samsung Introduces V10 BV-NAND With More Than 400 Layers
One of the more immediate announcements was Samsung’s V10 Bonding V-NAND, also known as BV-NAND.
The prototype uses a wafer-bonding architecture to connect the memory-cell wafer with the peripheral-circuit wafer. This allows Samsung to increase the number of layers without making the design unnecessarily bulky or difficult to manage.
V10 BV-NAND contains more than 400 layers. Samsung says the design increases memory density by around 58% compared with its previous V9 NAND generation. It also improves read, write and input/output performance.
That matters because modern AI systems are not only training large models. They are generating responses, retrieving information, running agents and processing huge databases in real time.
All of that creates storage pressure.
Traditional NAND flash is already used to store data in smartphones, servers and other computing devices. AI infrastructure needs much more of it, and it needs that storage to move data quickly without consuming an unreasonable amount of power.
zHBM Moves Memory Directly Above AI Accelerators
Samsung also showed a concept called zHBM, a more radical rethink of high-bandwidth memory.
Conventional HBM packages are generally positioned beside an AI processor. Samsung’s zHBM design stacks the memory vertically above the AI accelerator instead.
It sounds like a small change in placement. It is not.
Putting the memory closer to the processor reduces the physical distance that data must travel. Samsung expects this approach to increase bandwidth, reduce latency and improve energy efficiency during demanding AI workloads.
The company says a future interface using zHBM could provide roughly eight times the performance of HBM5. Its wafer-bonding technology could also support more than 10 times the memory density, triple energy efficiency and reduce thermal resistance by over 50%.
Those figures are based on Samsung’s projected architecture, not a commercially deployed product. Even so, the concept tackles one of the AI industry’s biggest technical problems: processors are becoming extremely fast, but moving data between processing and memory remains costly.
More computing power does not help much when the accelerator spends time waiting for data.
zNAND-O Targets Edge AI and Real-Time Workloads
Samsung’s second 3D memory concept, zNAND-O, focuses on high-performance NAND storage.
The technology is being developed in four-layer and eight-layer configurations. Samsung says it combines better space efficiency with lower latency and stronger input/output performance.
The likely target is edge AI.
That includes systems which process information close to where it is created rather than constantly sending it to a distant cloud data centre. Autonomous machines, industrial cameras, robotics platforms and real-time analytics tools all need quick access to local data.
Waiting for a remote server is not always practical. Sometimes it is not safe.
Samsung believes zNAND-O could support these data-heavy applications by packing more storage into a smaller space while allowing information to move faster.
Samsung Expands Its Wider AI Memory Roadmap
The FMS showcase was not limited to experimental designs.
Samsung also presented HBM4E, HBM5, LPDDR5X-PIM and enterprise storage products including the PM1763 and BM1773.
LPDDR5X-PIM places processing capabilities inside the memory itself. Instead of repeatedly moving information back and forth between memory and the main processor, certain operations can happen where the data is stored.
That can cut unnecessary data movement, which is one of the largest sources of energy use inside advanced AI systems.
The PM1763 takes a different role. It is a PCIe 6.0 enterprise solid-state drive designed for AI and high-performance computing servers. Samsung began mass-producing the drive in July 2026, using ninth-generation V-NAND, a 4-nanometre controller and support for liquid-cooled server environments.
Together, the products show Samsung trying to cover the full memory hierarchy rather than betting on one chip category.
AI servers need high-bandwidth memory near the processor. They also need system memory, fast enterprise storage and longer-term data capacity behind it.
A bottleneck in any one of those layers can slow the entire system.
AI Demand Is Reshaping the Memory Market
The timing is useful for Samsung.
Demand for AI infrastructure has tightened supplies of both DRAM and NAND products. Memory manufacturers are no longer dealing only with the familiar cycles of smartphone and PC sales. Large AI data centres are becoming major buyers, and their memory requirements are enormous.
Samsung recently said long-term customer agreements could eventually account for 60% to 70% of its memory sales. Analysts have also reported that DRAM and NAND inventories remain below historical levels despite concerns about weaker demand in some consumer markets.
That does not guarantee an endless boom. Memory remains a cyclical industry, and AI companies will keep looking for ways to reduce infrastructure costs.
The shift does give Samsung a reason to move quickly.
SK Hynix has built a strong position in high-bandwidth memory, while Micron is also expanding its AI-focused product portfolio. Samsung needs more than manufacturing scale to stay competitive. It needs architectures that customers can build future AI systems around.
zHBM and zNAND-O are not yet proof that Samsung has solved the problem. They are proof that the company knows where the pressure is building.
The next phase of the AI race may be decided less by which processor performs the most calculations and more by how efficiently the system can feed that processor with data.
