memory bandwidth hbm3
**HBM3** is the **third generation of High Bandwidth Memory that tripled per-pin data rates to 6.4 Gbps and introduced independent channel architecture** — delivering 819 GB/s per stack with 8-12 die stacking and up to 24 GB capacity, powering the current generation of AI training GPUs including NVIDIA's H100 and AMD's MI300X that are training the world's largest language models and generative AI systems.
**What Is HBM3?**
- **Definition**: The JEDEC JESD238 standard for third-generation High Bandwidth Memory — specifying 6.4 Gbps per pin, 1024-bit interface, 8-12 die stacking, and up to 24 GB per stack, with a redesigned channel architecture that provides true independent channels for improved bandwidth utilization.
- **Independent Channels**: HBM3 replaced HBM2's pseudo-channels with fully independent channels — each of the 16 channels has its own row buffer, command bus, and data bus, enabling simultaneous access to different memory banks without contention.
- **3.2× Speed Increase**: Per-pin data rate jumped from 2.0 Gbps (HBM2) to 6.4 Gbps (HBM3) — achieved through improved TSV signaling, on-die equalization, and advanced I/O circuit design.
- **12-High Stacking**: HBM3 extended stacking to 12 dies, increasing capacity to 24 GB per stack — enabled by thinner dies (~30 μm), improved TSV yield at higher stack counts, and advanced thermal solutions.
**Why HBM3 Matters**
- **AI Training Standard**: HBM3 is the memory technology in the GPUs training GPT-4, Claude, Gemini, and other frontier AI models — the NVIDIA H100 with 5 HBM3 stacks (80 GB, 3.35 TB/s) is the most deployed AI training accelerator.
- **Bandwidth Scaling**: HBM3's 819 GB/s per stack (3.2× over HBM2) keeps pace with the exponential growth of AI model sizes — larger models require proportionally more memory bandwidth to maintain training throughput.
- **HBM3E Extension**: SK Hynix and Samsung extended HBM3 to HBM3E with 9.6 Gbps per pin (1.18 TB/s per stack) — a 50% bandwidth increase within the same generation, deployed in NVIDIA H200 and B200.
- **Supply Constraint**: HBM3/3E demand from AI companies (NVIDIA, AMD, Google, Microsoft) far exceeds supply — SK Hynix, Samsung, and Micron are investing billions to expand HBM production capacity.
**HBM3 vs. HBM2 vs. HBM3E**
| Parameter | HBM2 | HBM3 | HBM3E |
|-----------|------|------|-------|
| Per-Pin Speed | 2.0 Gbps | 6.4 Gbps | 9.6 Gbps |
| BW per Stack | 256 GB/s | 819 GB/s | 1.18 TB/s |
| Stack Height | 4-8 dies | 8-12 dies | 8-12 dies |
| Capacity/Stack | 4-8 GB | 16-24 GB | 24-36 GB |
| Channels | 8 (pseudo) | 16 (independent) | 16 (independent) |
| Die Thickness | ~50 μm | ~30 μm | ~30 μm |
| Key GPU | V100/A100 | H100 | H200/B200 |
**HBM3 Key Products**
- **NVIDIA H100 (2022)**: 5× HBM3 stacks, 80 GB, 3.35 TB/s — the defining AI training GPU.
- **AMD MI300X (2023)**: 8× HBM3 stacks, 192 GB, 5.3 TB/s — largest HBM capacity in a single GPU.
- **NVIDIA H200 (2024)**: 6× HBM3E stacks, 141 GB, 4.8 TB/s — HBM3E upgrade of H100.
- **NVIDIA B200 (2024)**: HBM3E, 192 GB, 8 TB/s — next-generation Blackwell architecture.
**HBM3 is the memory backbone of the current AI revolution** — delivering the bandwidth and capacity that enable training of trillion-parameter language models and generative AI systems, with HBM3E extending performance further while the industry races to expand production capacity to meet insatiable AI demand.