Cooling Design Drives Competition Among Memory Makers

Chip designs that keep high-bandwidth memory (HBM) cool are emerging as a competitive battleground for memory makers supplying AI accelerators, according to The Korea Times. As vendors stack HBM taller and push data rates higher to feed AI workloads, the resulting heat increasingly limits how far the technology can scale, the report says. The Korea Times and The Korea Herald report that SK hynix has detailed an 'iHBM' architecture that builds cooling structures into the memory and cuts thermal resistance by more than 30 percent, while Samsung Electronics showed a heat-dissipation design it calls Heat Path Block on an HBM5 mockup at COMPUTEX 2026, per Tom's Hardware. Both approaches target the die-to-die interface where memory meets the processor, making thermal management a central point of differentiation among suppliers competing for AI hardware designs.
What happened
Chip designs aimed at keeping memory cool have emerged as a new competitive battleground for high-bandwidth memory (HBM) makers that supply AI accelerators, according to The Korea Times. The report describes Samsung Electronics and SK hynix each revealing heat-dissipation structures for their next-generation HBM, making thermal management a central point of differentiation among suppliers.
The thermal challenge
HBM vendors have been increasing the number of stacked memory dies and raising data-transfer speeds to meet demand for AI computing performance, The Korea Times reports. As stacks grow taller and faster, they generate more heat, and that heat increasingly limits how far the technology can scale. The Korea Herald reports the bottleneck is concentrated at the die-to-die interface that connects the memory stack to the processor.
Competing approaches
The Korea Times and The Korea Herald report that SK hynix has detailed a design it calls iHBM, which builds cooling structures directly into the memory and is said to cut thermal resistance by more than 30 percent while holding performance steady under high-load, high-temperature conditions. SK hynix plans to introduce the approach starting with the HBM5 generation, expected around 2029 to 2030, according to The Korea Herald and Tom's Hardware. Samsung Electronics, meanwhile, displayed a mockup of an HBM5 chip with a thermal solution it calls Heat Path Block at COMPUTEX 2026, Tom's Hardware reports.
Why it matters
Editorial analysis, industry pattern
In the HBM market, bandwidth and capacity have historically driven competition, but thermal performance is becoming a comparable differentiator as AI accelerators pack more memory closer to the processor. Suppliers that can demonstrate effective heat management may gain an edge in qualifying for next-generation accelerator designs, where sustained performance under dense, high-power conditions is a key requirement. Because integrated cooling changes how memory is built and packaged, it also carries implications for manufacturing complexity and cost.
What to watch
Editorial analysis
Signals to track include whether SK hynix and Samsung publish independent performance data for their cooling designs, how accelerator makers respond when specifying memory for future products, and whether the die-to-die interface remains the dominant thermal constraint as HBM5 nears production later this decade.
Key Points
- 1Heat-dissipation design has become a competitive axis for HBM makers as memory feeding AI accelerators runs hotter, per The Korea Times.
- 2Taller, faster HBM stacks generate more heat that now caps performance, pushing SK hynix and Samsung toward cooling built into the memory.
- 3Thermal architecture could shape which suppliers win AI accelerator designs, with SK hynix targeting its iHBM at HBM5 around 2029 to 2030.
Scoring Rationale
The story highlights hardware-level competition affecting memory integration for AI accelerators, relevant to system and chip designers but not broadly industry-shaking.
Sources
Public references used for this report.
Practice interview problems based on real data
1,625 SQL & Python problems across 15 industry datasets — the exact type of data you work with.
Try 250 free problems
