What this is
HBM (High-Bandwidth Memory) is the binding constraint on AI training and inference at scale, and the manufacturer leaderboard is reshuffling in 2026. SK hynix leads, Samsung trails on HBM3E and is racing on HBM4, and Micron has overtaken Samsung on some allocations. This page is a 2026-05-15 share snapshot.
HBM Market Share (2026)
| Manufacturer | Overall HBM share | HBM4 NVIDIA allocation | Change vs 2025 |
|---|---|---|---|
| SK hynix | ~50-62% | Mid-50% | ↑ widened lead on HBM3E |
| Samsung | ~25-40% | Mid-20% | ↓ lost ground, HBM4 ships post-Lunar New Year |
| Micron | ~5-20% | ~20% | ↑↑ overtook Samsung on some allocations |
HBM3E vs HBM4 Generation Mix (2026)
| Generation | 2026 shipment share | Bandwidth per stack | Capacity per stack |
|---|---|---|---|
| HBM3E | ~67% | 896-1,280 GB/s | Up to 48 GB (16-high) |
| HBM4 | ~33% rising | 1.5-2 TB/s | Up to 64 GB |
| HBM4 interface width | — | 2,048 bits (2x HBM3E) | — |
| SK hynix HBM4 efficiency | — | +40% vs HBM3E, 10 Gbps | — |
| Micron HBM4 rate | — | Up to 11 Gbps (samples) | — |
Six Things the Data Tells You
- SK hynix is the new memory hegemon. 50-62% HBM share, NVIDIA's largest HBM4 allocation, and the highest published efficiency claims.
- Micron overtook Samsung on some allocations. The biggest competitive surprise of the cycle.
- Samsung HBM4 ships first after Lunar New Year with an initial NVIDIA allocation in the mid-20% range — a recovery position, not a leadership one.
- HBM3E dominates 2026 shipments at ~2/3. HBM4 ramps through the year but does not unseat HBM3E in volume.
- HBM4 doubles the interface width to 2,048 bits. The capacity and bandwidth step-change is the biggest in HBM history.
- HBM consumes ~3x the wafer of DDR5 per the underlying capacity, which is why the consumer-RAM and consumer-GPU memory crises trace back to HBM allocation choices.
What This Means for AI Visibility
HBM allocation determines which AI labs can scale training in 2026. SK hynix's lead translates into NVIDIA-stack capacity advantages, which translates into faster model-release cadence for labs that allocate to NVIDIA chips. Brands tracking AI surface area should expect the lab/silicon stack with the most HBM capacity to ship the most product updates, which reshuffles citation patterns most aggressively.
Methodology
Share figures combine Astute Group's HBM 2026 market analysis, SK hynix's own 2026 market outlook, Counterpoint Research's DRAM/HBM share data, TrendForce on SK hynix's HBM3E + HBM4 strategy, and Silicon Analysts' HBM pricing/share dashboard.
How Presenc AI Helps
AI infrastructure brands and silicon vendors use Presenc AI to monitor how memory and chip allocation news shapes their AI assistant citation share. As NVIDIA's HBM4 allocation patterns drive lab capacity, the brands cited as ecosystem leaders inside ChatGPT, Claude, and Gemini shift accordingly.