Samsung Electronics is attempting to reclaim lost ground in the artificial intelligence memory race by moving aggressively into next-generation high-bandwidth memory, unveiling sample shipments of its new 12-layer HBM4E chips months ahead of broader market adoption.
The announcement marks a critical moment for Samsung, which has spent the past two years watching rivals SK Hynix and Micron Technology capture much of the explosive demand tied to AI servers and accelerators, particularly within the supply chain of Nvidia.
Samsung said the new HBM4E chips are more than 20% faster than its previous HBM4 generation and are built using its sixth-generation 10-nanometer-class DRAM process alongside a 4-nanometer logic base die manufactured through its foundry business.
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The move has put the industry on notice because the AI semiconductor race is increasingly being shaped not just by compute chips themselves, but by access to advanced memory capable of feeding enormous volumes of data into AI accelerators fast enough to avoid bottlenecks.
HBM has become one of the most critical technologies in modern AI infrastructure. Advanced AI systems from Nvidia, AMD, and Google rely heavily on stacked memory architectures that deliver dramatically higher bandwidth and energy efficiency than conventional DRAM. That has transformed HBM from a niche product into one of the semiconductor industry’s most profitable and strategically important segments.
Samsung’s latest move comes only three months after it began shipping HBM4 samples, an unusually fast progression that signals urgency inside the company to close the gap with SK Hynix, which currently dominates the market.
According to Counterpoint Research, SK Hynix held 57% of the global HBM market in the fourth quarter of 2025, compared with Samsung’s 22% and Micron’s 21%.
Analysts say timing matters enormously in the HBM business because early suppliers often lock in multiyear customer relationships tied to AI infrastructure buildouts worth tens of billions of dollars.
“In the HBM market, early movers tend to secure the bulk of orders,” said Jeff Kim of KB Securities-Jefferies, pointing to Samsung’s delayed entry into earlier HBM3 and HBM3E cycles.
The stakes are especially high because hyperscalers and AI model developers are now racing to secure long-term memory supply amid fears of shortages as AI compute demand accelerates globally.
Samsung’s customer list already includes major AI players such as Advanced Micro Devices, Nvidia, and Google. But qualification by Nvidia remains the industry’s most important benchmark because Nvidia systems dominate the AI training market.
Samsung shares rose as much as 6.5% following the announcement, substantially outperforming South Korea’s broader market, as investors bet the company may finally be regaining momentum in AI semiconductors.
Part of that optimism also stems from Samsung’s expanding role beyond memory.
Earlier this year, AI company Anthropic identified Samsung as a strategic infrastructure partner in a funding round that valued the startup at $965 billion. Notably, Samsung was singled out not only for memory capabilities but also for logic-chip manufacturing, highlighting growing expectations that the company could become a larger beneficiary of the AI infrastructure boom through its foundry business.
That matters because Samsung is one of the very few companies globally capable of manufacturing advanced chips at scale outside of Taiwan Semiconductor Manufacturing Company.
With TSMC’s advanced-node capacity expected to remain heavily booked for years amid surging AI demand, some analysts believe Samsung could increasingly emerge as an alternative manufacturing partner for AI chip designers seeking additional supply flexibility. The company already strengthened that narrative last year after announcing a $16.5 billion chip supply agreement with Tesla.
Samsung’s broader challenge, however, goes beyond simply launching newer products. The company must convince customers that it can consistently deliver top-tier yields, power efficiency, and manufacturing reliability in an industry where execution failures can delay entire AI server deployments.
That is particularly important in HBM, where packaging complexity has become nearly as important as transistor performance. Advanced HBM stacks require precise thermal management, ultra-dense interconnects, and high production yields to meet hyperscaler requirements.
Samsung’s aggressive HBM4E rollout is seen as an indication that the company understands the path the next phase of the AI boom is taking. Analysts believe the next phase may be determined not only by who designs the most powerful AI chips, but by who controls the surrounding memory and manufacturing ecosystem needed to keep those chips running at scale.
In effect, the battle over AI dominance is increasingly becoming a supply-chain war.