Samsung just fired the opening salvo in the next-gen AI memory wars. The company has begun shipping commercial HBM4 chips to customers, marking the first time anyone's delivered the next-generation high-bandwidth memory that powers AI datacenter infrastructure. With speeds hitting 11.7 Gbps - 46% faster than the industry standard - Samsung's betting its early lead will reshape the AI hardware landscape and triple its HBM sales this year.
Samsung isn't waiting around. While competitors were still refining their designs, the Korean tech giant started mass-producing and shipping its HBM4 chips - the specialized memory that sits at the heart of AI accelerators and datacenter GPUs. It's a bold move that could cement Samsung's position in a market where every nanosecond of data transfer matters.
The announcement comes as hyperscalers and GPU manufacturers scramble to secure memory supplies for their next-generation AI systems. According to Samsung's newsroom, the company achieved this milestone by skipping the conservative approach - instead of using proven older tech, they jumped straight to their most advanced 6th-generation 10nm-class DRAM process (1c) and 4nm logic.
"Instead of taking the conventional path of utilizing existing proven designs, Samsung took the leap," Sang Joon Hwang, Executive Vice President and Head of Memory Development at Samsung, told reporters. The gamble appears to have paid off. The company says it hit stable yields and industry-leading performance right out of the gate, with no redesigns needed.
The specs tell the story. Samsung's HBM4 consistently processes data at 11.7 gigabits per second, blowing past the 8 Gbps industry baseline by roughly 46%. That's also a 22% jump over HBM3E's maximum pin speed of 9.6 Gbps. But Samsung isn't stopping there - the chips can be pushed to 13 Gbps, creating headroom as AI models balloon in size and complexity.
Total memory bandwidth per stack has jumped 2.7 times compared to HBM3E, maxing out at 3.3 terabytes per second. For context, that's the equivalent of streaming thousands of 4K movies simultaneously - except it's happening between a GPU and its memory thousands of times per second. In AI training and inference workloads, where data bottlenecks can cripple performance, that bandwidth translates directly to faster model training and lower latency.
