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HBM4 offers much higher advertised bandwidth per stack than HBM3E, but that alone does not make an accelerator the better buy. Capacity, stack configuration, power and thermal behavior, platform compatibility, availability, and performance on your actual workload all matter. The figures below are supplier specifications and announcements, not independent system benchmarks.
What HBM is—and what the generation labels mean
High-bandwidth memory (HBM) is specialized DRAM built by stacking memory dies vertically and connecting them with through-silicon vias (TSVs). Its wide interface and compact package are designed to move data at high rates. HBM is typically integrated into AI and high-performance computing accelerators; it works alongside system memory such as DDR5 or LPDDR rather than replacing it.
HBM is the general technology name, not one specific generation. HBM3E and HBM4 identify newer generations of that technology. A product’s performance still depends on its particular design and configuration, so a generation label is not a guarantee that every product reaches the same bandwidth or capacity.
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Bandwidth measures data transferred per second; capacity measures how much data memory can hold. A stack’s bandwidth is not the accelerator’s total bandwidth: the accelerator may use multiple stacks, and platform design affects the aggregate.
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| Generation and supplier | Bandwidth per stack | Capacity and configuration stated by supplier | What the claim establishes |
|---|---|---|---|
| HBM3E — Micron | More than 1.2 TB/s | No single universal capacity stated on the reviewed portfolio page. | Micron’s product figure; not a guarantee for every HBM3E design. |
| HBM3E — Samsung | Up to 1,180 GB/s | 24 GB and 36 GB; 8-high and 12-high stack options. | Samsung’s product maximum. It is broadly similar in scale to Micron’s figure, but the products and measurement terms are not identical. |
| HBM4 — Micron | More than 2.8 TB/s | 36 GB 12-high HBM4; Micron also identifies 48 GB 16-high customer samples in 2026. | The sample description is not evidence of broad market availability. |
| HBM4 — Samsung | Up to 3,300 GB/s | 36 GB 12-high on its product page. Its February 2026 announcement described 24–36 GB 12-layer options and plans for up to 48 GB with 16-layer stacking. | Samsung reported mass production and commercial shipments in February 2026; stated maxima and configurations are product-specific. |
| HBM4 — SK hynix | Over 10 Gbps operating speed; the cited announcement does not give a per-stack bandwidth figure. | 2,048 I/O terminals. | SK hynix said in September 2025 that development was complete and preparation for mass production was ready. This is a supplier milestone, not an independent benchmark. |
These numbers should not be collapsed into a single universal “HBM3E speed” or “HBM4 speed.” Micron’s figures are in TB/s and Samsung’s in GB/s; the suppliers describe product-specific figures and maxima. In the cited Samsung announcement, the company reported HBM4 at a consistent 11.7 Gbps, up to 13 Gbps, and up to 3.3 TB/s per stack. Samsung also said its 11.7 Gbps figure exceeded an 8 Gbps industry standard by approximately 46%—a company-reported comparison.
Why HBM4 can offer more bandwidth
Samsung and SK hynix describe HBM4 as using a 2,048-I/O interface, double the 1,024-pin interface they cite for the previous generation. A wider interface can carry more data in parallel. That architectural change helps explain the higher supplier bandwidth claims, but it does not by itself establish how much faster an AI application will run.
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- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
Power-efficiency figures also need attribution. Samsung reported a 40% improvement compared with HBM3E in its 2026 announcement. SK hynix reported more than 40% improvement compared with the previous generation in its 2025 announcement. These are supplier comparisons; they are not independently verified, controlled measurements of complete accelerator systems.
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What buyers should compare when choosing an accelerator
HBM is generally integrated into the accelerator package, so buyers usually choose a platform that includes a particular memory configuration rather than selecting a standalone HBM stack. Compare the accelerator and system as a whole.
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- Capacity per accelerator: Check whether the total HBM capacity supports the model, batch size, context length, and workload you intend to run. Capacity is not bandwidth, and more of one does not automatically compensate for too little of the other.
- Aggregate bandwidth and configuration: Confirm the number of stacks, capacity per stack, stack height, and total supported configuration. Do not treat one stack’s advertised maximum as the accelerator’s total throughput.
- Power and thermal behavior: Ask for platform-level measurements under the workload and deployment conditions you expect. A supplier’s component-efficiency claim does not establish system power, cooling needs, or operating cost.
- Compatibility and delivery: Verify the exact HBM generation and capacity supported by the accelerator and system vendor, along with the delivery schedule for the specific configuration you need.
- Workload results and total cost: Compare measured throughput, latency, utilization, and cost using the intended model and software stack. A per-stack bandwidth figure cannot supply those results.
Availability: what supplier milestones do and do not tell you
Supplier announcements show that HBM4 has moved beyond development for some vendors, but they do not prove equivalent qualification, volume, shipment, or availability across suppliers or accelerator platforms. Samsung announced mass production and commercial shipments in February 2026. SK hynix said in September 2025 that it had completed development and was prepared for mass production. Micron’s portfolio information describes HBM4 as in high-volume production and identifies 2026 customer samples of a 48 GB 16-high product. These are separate supplier statements about different milestones and configurations; confirm availability and qualification for the exact platform you are considering.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does HBM4 make an accelerator faster than HBM3E?
HBM4 suppliers advertise substantially higher bandwidth per stack than their HBM3E figures. That makes HBM4 promising for workloads that are limited by memory bandwidth, but it does not establish a universal application-level performance gain. No neutral, controlled, cross-vendor HBM3E-versus-HBM4 system benchmark is established by these supplier specifications. Accelerator design, memory capacity and configuration, workload, software, and system setup all affect results. Use platform-level measurements for the work you need to do, rather than calculating an application speedup from the stack bandwidth ratio.
Quick Recap
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- 24GB GDDR6 on 192-Bit Bus: Massive 24GB memory with 456 GB/s bandwidth – ideal for LLMs, AI inference, 3D rendering, and generative design.
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