Samsung Unveils HBM4E and Full-Stack AI Solutions at GTC 2026
Samsung Electronics used NVIDIA GTC 2026 in San Jose, California, to showcase a broad portfolio spanning high-bandwidth memory, server memory, enterprise storage, advanced packaging, semiconductor manufacturing, and edge-AI components.
The company’s positioning goes beyond individual semiconductor products. Samsung is one of the few major semiconductor vendors with capabilities spanning memory, logic, foundry, and advanced packaging, allowing it to address multiple layers of the AI computing stack.
The centerpiece of the exhibition was Samsung’s next-generation memory roadmap, including mass-produced HBM4 and the first public unveiling of HBM4E. The company also highlighted hybrid copper bonding, NVIDIA-oriented server technologies, AI-driven semiconductor manufacturing, and low-power memory designed for on-device AI.
🧠 HBM4 Enters Mass Production #
Samsung announced that its sixth-generation HBM4 has entered full mass production and is being positioned for next-generation NVIDIA AI platforms, including the Vera Rubin architecture.
HBM4 represents a significant increase in memory bandwidth compared with previous HBM generations. Samsung’s implementation is based on its sixth-generation 10nm-class DRAM process, commonly identified as 1c DRAM.
HBM4 performance targets #
Samsung reports that its HBM4 delivers a sustained transfer rate of 11.7 Gbps per pin, with scalability to approximately 13 Gbps per pin.
This exceeds the commonly cited 8 Gbps baseline associated with HBM4-class products and provides substantially more aggregate bandwidth when multiple HBM stacks are integrated around an AI accelerator.
For large-scale AI workloads, the additional bandwidth is particularly important because accelerator compute throughput increasingly depends on how quickly data can be supplied from high-bandwidth memory.
Why HBM bandwidth matters #
Modern AI accelerators can contain extremely large numbers of compute units, but those units can become underutilized when memory bandwidth cannot keep pace with computational demand.
HBM addresses this bottleneck by placing high-density DRAM stacks close to the accelerator package and providing a very wide interface between memory and compute.
As AI models become larger and inference workloads increasingly involve high-throughput memory access, HBM bandwidth, capacity, power efficiency, and thermal performance are becoming critical system-level design parameters.
⚡ Samsung Debuts HBM4E #
Samsung also introduced HBM4E, providing an early look at the company’s next step in high-bandwidth memory technology.
According to Samsung, HBM4E can reach pin speeds of up to 16 Gbps, with aggregate stack bandwidth reaching as high as 4.0 TB/s.
This represents another substantial increase in bandwidth density and reinforces the industry’s broader shift toward increasingly aggressive HBM performance targets for AI accelerators.
HBM4E and next-generation AI accelerators #
Higher HBM bandwidth is particularly relevant as accelerator architectures scale in compute density.
However, increasing interface speeds is not simply a matter of improving DRAM signaling. Higher data rates place additional pressure on:
- Signal integrity
- Power delivery
- Thermal dissipation
- Package interconnects
- Memory-controller architecture
- Manufacturing yield
Consequently, future HBM generations require coordinated improvements across DRAM process technology, stacking, bonding, package design, and system architecture.
🔩 Hybrid Copper Bonding Targets HBM Thermal Bottlenecks #
One of Samsung’s most important packaging demonstrations was its Hybrid Copper Bonding (HCB) technology for high-layer-count HBM stacks.
Samsung highlighted HCB for 16-layer and higher HBM configurations, where thermal management becomes increasingly difficult as more DRAM dies are vertically integrated.
HCB versus Thermo-Compression Bonding #
Traditional Thermo-Compression Bonding (TCB) remains widely used for stacked memory assembly. However, increasing the number of layers can amplify thermal resistance and mechanical constraints within the stack.
Samsung claims that its HCB approach can reduce thermal resistance by more than 20% compared with conventional TCB.
Lower thermal resistance can provide additional thermal headroom for high-bandwidth memory operating under sustained AI workloads.
This is becoming increasingly important because HBM is no longer an isolated memory component. It is part of a tightly integrated accelerator package in which memory, compute dies, interposers or bridges, and thermal-management structures all interact.
🤝 Deeper Hardware Collaboration With NVIDIA #
Samsung also used a dedicated NVIDIA Gallery exhibit to demonstrate hardware aligned with NVIDIA’s AI server ecosystem.
The showcase included server memory and enterprise storage technologies designed to address bandwidth, capacity, and data-movement requirements in next-generation AI infrastructure.
SOCAMM2 reaches mass production #
Samsung announced high-volume mass production of SOCAMM2, a server memory module based on low-power DRAM.
The company described the achievement as an industry-first mass-production milestone for this class of memory technology.
SOCAMM2 is designed to provide high memory bandwidth while offering greater flexibility for AI server system integration.
As AI servers increasingly require large amounts of memory outside the accelerator’s HBM subsystem, specialized server memory architectures can complement HBM by providing additional capacity for workloads that do not require the extreme bandwidth of on-package memory.
PCIe 6.0 enterprise storage #
Samsung also demonstrated its PM1763 PCIe 6.0 SSD on servers using NVIDIA’s SCADA programming model.
The company additionally showcased the PM1753 SSD within NVIDIA’s BlueField-4 STX reference architecture.
High-speed storage is becoming increasingly relevant to AI infrastructure because system performance depends not only on accelerator throughput but also on how efficiently data can be loaded, cached, moved, and persisted.
For inference systems in particular, storage and networking bottlenecks can affect accelerator utilization when model weights and supporting datasets cannot be supplied quickly enough.
🏭 Samsung Builds AI Factories With NVIDIA #
Samsung’s GTC presentation also extended beyond semiconductor components into manufacturing.
The company described its collaboration with NVIDIA to develop semiconductor AI Factories, combining accelerated computing with digital-twin technologies and NVIDIA Omniverse libraries.
Samsung is applying these technologies across multiple areas of its semiconductor operations, including memory, logic, foundry, and advanced packaging facilities.
Digital twins for semiconductor manufacturing #
Digital twins allow physical manufacturing environments and processes to be represented computationally.
For semiconductor production, this can enable engineers to simulate and optimize complex operations before applying changes to physical equipment.
Potential applications include:
- Equipment and factory simulation
- Process optimization
- Computational lithography
- Electronic design automation
- Production scheduling
- Automated fab operations
- Predictive analysis
- Manufacturing workflow optimization
At GTC 2026, Samsung executive Yong Ho Song presented practical examples of this transformation in a keynote focused on applying agentic AI across semiconductor design, engineering, and production.
The broader objective is to move AI from an accelerator workload into the manufacturing process itself.
💻 Memory for Personal and Edge AI #
Samsung’s AI strategy extends beyond hyperscale data centers. The company also presented memory and storage technologies intended for personal computing, mobile devices, wearables, and other edge-AI systems.
Storage for personal AI supercomputing #
Samsung highlighted its PM9E3 and PM9E1 NAND storage products in conjunction with NVIDIA DGX Spark systems.
High-performance local storage becomes increasingly important as personal AI systems move toward running larger models locally rather than relying entirely on cloud inference.
Local AI workloads can generate substantial storage requirements for model weights, datasets, vector databases, applications, and cached inference data.
LPDDR5X for mobile AI #
Samsung also highlighted LPDDR5X, with transfer rates reaching up to 25 Gbps per pin.
The company reports power consumption reductions of up to approximately 15%, depending on implementation.
For smartphones, wearables, and other battery-powered devices, the combination of bandwidth and power efficiency is particularly important. Local AI inference can require sustained memory activity, making DRAM power consumption a significant component of overall system energy usage.
🔮 Samsung Previews LPDDR6 #
Looking further ahead, Samsung previewed LPDDR6, targeting single-pin transfer rates in the range of 30–35 Gbps.
The next-generation memory technology is also expected to introduce more advanced power-management mechanisms, including adaptive voltage regulation and dynamic refresh controls.
LPDDR6 and edge-AI workloads #
The goal is to increase memory bandwidth without allowing memory power consumption to scale proportionally.
This is particularly important for edge AI, where compute resources are constrained by battery capacity, thermal limits, device size, and sustained power budgets.
Higher memory bandwidth can support more demanding on-device models, while adaptive voltage and refresh mechanisms can help reduce unnecessary energy consumption during workloads with variable memory activity.
🌐 Samsung’s Broader AI Semiconductor Strategy #
Samsung’s GTC 2026 showcase demonstrates that the competition for AI infrastructure is expanding beyond accelerator compute performance.
Memory bandwidth, advanced packaging, storage, manufacturing automation, and power efficiency are becoming increasingly interconnected.
The company’s HBM4 and HBM4E roadmap addresses the bandwidth requirements of large AI accelerators. HCB targets the thermal challenges associated with increasingly dense HBM stacks. SOCAMM2 and PCIe 6.0 SSDs address server-level memory and storage requirements, while AI Factory initiatives apply accelerated computing and agentic AI directly to semiconductor manufacturing.
At the edge, LPDDR5X and the upcoming LPDDR6 generation target the rapidly growing demand for local AI inference under strict power constraints.
Taken together, these technologies illustrate a broader shift in semiconductor competition: the AI hardware stack is becoming increasingly vertically integrated, and memory, packaging, manufacturing, and system architecture are now strategic differentiators alongside raw compute performance.