The next big race in Artificial Intelligence is no longer just about GPU power, but about the memory that fuels them. Specifically, it’s about HBM (High Bandwidth Memory), the component that has become an industrial bottleneck and a differentiating factor for training and inference of increasingly larger models. In this landscape, Samsung aims to re-enter the spotlight with HBM4, a generation promising higher bandwidth and, according to recent reports, has already passed key validation stages with top-tier clients.
South Korean media indicate that Samsung has completed the final quality testing phases for HBM4 with NVIDIA and AMD, aiming to start mass production shortly and begin positioning its modules within the NVIDIA ecosystem for the upcoming AI platform known as Vera Rubin. Simultaneously, initial public demonstrations are expected at GTC 2026, NVIDIA’s major tech event, where Rubin is expected to play a starring role.
Why HBM4 Matters More Than Ever
The significance of HBM4 can be understood with a simple idea: in AI accelerators, performance depends not only on computational capacity but also on how much data can enter and exit memory at high speeds. HBM is designed precisely for this purpose: providing an extreme bandwidth thanks to its physical proximity to the processor and a very wide interface.
Moreover, HBM4 brings a substantial technical evolution: the industry specification includes a 2,048-bit interface. This jump allows, in terms of design, to double data throughput compared to previous generations—assuming pin speeds and power efficiency keep pace. This combination—wider interface and higher speeds—is elevating the competitive pulse among Samsung, SK hynix, and Micron.
The Industry’s “Goal”: Surpassing Standards and Meeting NVIDIA’s Demands
One of the most sensitive points for this generation is the pin speed, measured in gigabits per second (Gbps). The JEDEC standard for HBM4 initially cited around 8 Gbps per pin. However, according to industry sources, NVIDIA has pushed suppliers to go further, targeting figures of 10 Gbps or higher to sustain the expected performance leap in Rubin.
Within this context, Korean media report that Samsung would achieve an operational speed of 11.7 Gbps in HBM4, exceeding the industry’s minimum target. This achievement would not only be a technical victory but also position Samsung as a capable supplier to meet expected massive demand, especially as memory becomes “gold” for AI accelerators.
Schedule: From Initial Units to Supply Ramp-Up
Reports from Korea outline a clear timeline:
- Short-term production after completing final testing with clients.
- Integration for demonstrations at GTC 2026 (March).
- Supply ramp-up mainly around June, depending on final chip manufacturing schedules and production rates of integrators.
This nuance is key: in HBM, producing a fast module isn’t enough. It must be manufactured at volume—with wafer yield and stacking performance that meet standards—and the entire assembly and validation chain must be aligned with the customer. The industry has learned in recent years that bandwidth is irrelevant if it arrives too late.
The “Base Die” Factor: When Memory Also Depends on Logic
Another emerging element in HBM4 is the so-called logic base die (the logic layer at the base of the stack). In this generation, the base die becomes more critical for managing data movement, efficiency, and integration. Market reports suggest Samsung might be using a 4 nm fabric-style base die within its own foundry division, giving it greater control over timelines and supply capacity—compared to competitors who may rely more heavily on third-party manufacturing for this component.
In other words, the HBM4 battle is no longer just “memory versus memory.” It’s also a contest of advanced packaging, industrial yields, and capacity planning, in a world where the same equipment and factories compete for AI, consumer electronics, and automotive markets.
Parallel Competitors: SK Hynix and Micron Also Advancing
The progress attributed to Samsung isn’t happening in isolation. SK hynix has announced significant advancements in HBM4 recently, including speeds surpassing the standard and preparations for volume manufacturing. Likewise, Micron has reported sending out HBM4 samples with pin speeds above 11 Gbps and impressive bandwidth figures—underscoring that the next generation of memory will see tight competition, with no clear dominant yet.
For NVIDIA, this rivalry is strategic: more suppliers arriving on time and at volume reduces supply constraints and enhances the ability to tailor configurations per product, market, and cost. For Samsung, the challenge is twofold: demonstrate that its technical performance translates into sustained supply and recover ground after a period where competitors had perceived advantages in initial HBM phases.
What’s Truly at Stake
In the short term, headlines revolve around Rubin, GTC, and two-digit Gbps speeds per pin. But the larger issue is who controls AI’s underlying hardware infrastructure. HBM memory influences accelerator design, margins, availability, and ultimately the pace at which large-scale training and inference data centers are deployed.
If Samsung establishes HBM4 as a reliable and competitive supply source for Rubin, it won’t just secure a contract; it will secure a position of influence in the most decisive technological cycle in the semiconductor industry this decade.
Frequently Asked Questions
What is HBM4 and why is it so important for AI GPUs?
HBM4 is a new generation of high-bandwidth memory designed to power AI accelerators with more data per second. In training and inference workloads, memory bandwidth can be as critical as compute power.
What does “11.7 Gbps per pin” in HBM4 mean?
It indicates the data transfer rate per data line of the interface. Higher Gbps per pin—combined with a wide interface—means greater total bandwidth available to the GPU or accelerator.
Why is validation and “verification stage” with NVIDIA and AMD so important?
Because manufacturing memory isn’t enough; it must pass compatibility, stability, and performance tests by customers, along with meeting thermal and reliability standards in real data center scenarios.
When might HBM4’s impact be felt in the accelerator and data center markets?
If production and supply timelines are met, 2026 is expected to be the year HBM4 appears in new AI platforms, affecting hardware availability, delivery schedules, and server configurations.
via: biz.sbs.co.kr and wccftech