HBM stacks DRAM dies vertically, linked by through-silicon vias (TSVs) next to the GPUCITE:E1. This design — multiple DRAM dies bonded on top of one another and connected through TSVs on a silicon interposer sitting beside the GPU or accelerator — trades a conventional wide-and-flat memory layout for an extremely wide, extremely short data channelCITE:E1. The reason AI accelerators need this architecture comes down to the "memory wall": compute units process data far faster than memory can supply it, so when bandwidth can't keep pace, even the most powerful processor sits idle waiting for dataCITE:E2. HBM's wide, chip-adjacent channel exists specifically to close that gapCITE:E2.
HBM bandwidth rose from 819 GB/s per stack in HBM3 to 2 TB/s in HBM4CITE:E3. The generation in between, HBM3E, reached roughly 1.2 TB/s per stack using a 12-layer, 36GB configurationCITE:E3. HBM4, standardized by JEDEC in 2025, pushes per-stack bandwidth to roughly 2 TB/sCITE:E3.
| Generation | Bandwidth per stack | Configuration |
|---|---|---|
| HBM3 | ~819 GB/s | — |
| HBM3E | ~1.2 TB/s | 12-layer, 36GB |
| HBM4 (2025 JEDEC standard) | ~2 TB/s | — |
Each generational jump exists to keep pace with newer AI chips that would otherwise run into the same memory-wall limitCITE:E2CITE:E3.
SK Hynix holds 58% of the HBM market, with Samsung Electronics and Micron at 21% eachCITE:E4. That breakdown, drawn from Q1 2026 supplier-share data, means only three companies worldwide currently produce HBM at allCITE:E4. This concentration is a direct factor behind AI chip shortages and pricing pressure, since the entire market's HBM output depends on just these three suppliers' capacityCITE:E4.
SK Hynix, Samsung Electronics, and Micron have all secured NVIDIA certification for HBM4CITE:E5. The certification corresponds to NVIDIA's upcoming Vera Rubin platformCITE:E5. With all three suppliers now qualified, the determining factor for AI chip shipment timing shifts to which company can ramp HBM4 volume production first and sustain the highest yieldCITE:E5.
HBM manufacturing grows harder as stack height and TSV density increaseCITE:E6. Taller stacks and denser TSV connections raise both fabrication complexity and yield riskCITE:E6. HBM also cannot reach the GPU on its own — it requires advanced packaging, such as TSMC's CoWoS, to physically integrate with the processorCITE:E6. As a result, what looks like a "GPU shortage" is frequently, in practice, a shortage of HBM supply and advanced packaging capacityCITE:E6.
The same three companies that supply nearly all HBM — SK Hynix at 58%, Samsung Electronics and Micron at 21% eachCITE:E4 — have also all cleared NVIDIA's HBM4 certification for Vera RubinCITE:E5, meaning the next bottleneck is not qualification but each supplier's stacking yield and TSV manufacturing capacityCITE:E6. Because HBM must still pass through advanced packaging like CoWoS before it reaches a GPUCITE:E6, bandwidth gains from HBM3's 819 GB/s to HBM4's 2 TB/s per stackCITE:E3 only translate into AI chip shipments as fast as packaging capacity allows.
HBM stacks DRAM dies vertically, linked by through-silicon vias (TSVs) next to the GPUCITE:E1.
HBM bandwidth rose from 819 GB/s per stack in HBM3 to 2 TB/s in HBM4CITE:E3. The generation in between, HBM3E, reached roughly 1.
SK Hynix holds 58% of the HBM market, with Samsung Electronics and Micron at 21% eachCITE:E4.
SK Hynix, Samsung Electronics, and Micron have all secured NVIDIA certification for HBM4CITE:E5.
The numbers line up into one structural point: HBM4 certification is now a solved problem for all three suppliers, but the 58%/21%/21% split means SK Hynix's stacking yield alone has outsized influence over how fast the 2 TB/s HBM4 generation actually ships. Because CoWoS-style packaging sits between any finished HBM stack and a usable GPU, capacity at that packaging step is as much a gating factor as DRAM output itself. The metric worth watching next is which supplier moves from certification to high-volume HBM4 shipment first — that, more than the certification itself, will set the real pace of next-generation AI chip availability.
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