SEOUL, South Korea / RankWire.AI / – Samsung Electronics revealed its advanced memory and storage strategies for artificial intelligence infrastructure during the 2026 OCP Global Summit. The company showcased its plans at the event held in San Jose, California, from October 12 to 15. The presentation covered three technological directions: high-performance memory, Compute Express Link-based memory expansion, and next-generation solid-state drives. Additionally, Samsung announced a joint CXL demonstration with Advanced Micro Devices. The overarching goal of this strategy is to address memory bottlenecks that hinder the performance of larger AI workloads.
Samsung organized its approach around three core principles: speed, scalability, and data durability. The first pillar emphasizes high-performance memory, with Samsung displaying customized high-bandwidth memory and HBM5, its eighth-generation HBM. The second pillar leverages CXL technology to extend memory capacity beyond what is available within individual servers. The third pillar concentrates on data storage and management through cutting-edge enterprise SSDs and advanced storage software. Samsung affirms that these three areas work synergistically to enhance overall AI system performance.
The company connected this strategy to the growing demand for memory driven by AI inference workloads and larger models. As models expand, they generate increased data during inference, necessitating more memory to store it. Current server designs often limit the amount of memory accessible to each processor. Samsung proposes CXL as a solution to surpass these constraints without requiring a complete system redesign. The company also emphasized the importance of key-value cache optimization, which enables AI models to reuse previously processed data instead of recomputing it.
Samsung and AMD showcase CXL memory expansion
At the summit, Samsung and AMD jointly demonstrated a CXL-based memory expansion solution. This technology enables processors to share memory resources and scale capacity flexibly across AI systems. Samsung reports that the solution has already been validated in real-world system environments. The two firms signed a memorandum of understanding in March 2026, focusing on next-generation AI memory and computing innovations. Furthermore, AMD CEO Lisa Su visited South Korea on October 7 to discuss collaboration in memory and foundry services.
Samsung also introduced CMM-D, its CXL-compatible DRAM memory module designed for memory pooling. This setup links multiple CXL memory modules into a shared pool, allowing different systems to access and utilize the resources as needed. Other exhibits included processing-in-memory technology, which performs certain calculations within memory chips to reduce data transfer needs. Samsung displayed 2-nanometer gate-all-around wafers and Groq LPU wafers. Nvidia’s Groq 3 LPX system was also among the exhibits showcased by Samsung.
Partners gain early access to memory validation programs
Shim Ho-jun, Corporate Vice President and Head of Samsung’s Application Engineering Team, delivered the strategy during an executive session. He explained that expanding memory capacity and bandwidth has become increasingly critical as AI models grow larger and inference demands intensify. Shim noted that Samsung is increasing co-design and technical validation efforts with global partners early in the development process. This approach aims to ensure that memory products align with customer system requirements before reaching the market.
Samsung MAX Lab supports this initiative by allowing customers to test memory and storage solutions under real AI workloads. This enables validation of technologies such as PIM and next-generation SSDs prior to commercialization. Samsung participated in the summit as a Diamond Sponsor of the Open Compute Project event. This appearance follows Samsung’s presentations at Nvidia GTC in March, COMPUTEX in June, and the Future of Memory and Storage conference in August. The company continues to emphasize memory capacity as a critical factor in AI infrastructure performance.