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NVIDIA’s AI GPUs do the calculations; high-bandwidth memory (HBM) supplies the model data those calculations need. HBM is DRAM integrated into the GPU package, where it can move data to and from the processor at high bandwidth. Micron has publicly linked its HBM3E to specific NVIDIA platforms, including H200 and named Blackwell systems—but that does not mean it supplies memory for every NVIDIA GPU.
What HBM does inside an AI GPU
An AI model uses data such as weights and intermediate results, or activations. NVIDIA GPUs contain many processing elements, including streaming multiprocessors and specialized compute hardware, that perform operations on this data. The compute engines are not the memory: HBM is the nearby, high-capacity DRAM that holds data the GPU needs while it works.
Data moves through a hierarchy. Small, fast on-chip caches sit close to the compute units; larger amounts of data reside in HBM, which is off-chip DRAM mounted in the GPU package. The GPU fetches data from HBM through cache for execution, then writes results back. NVIDIA describes the GPU as “a highly parallel processor architecture, composed of processing elements and a memory hierarchy” in its GPU Performance Background User’s Guide.
Capacity and bandwidth solve different problems
- Capacity is how much data can fit in HBM at once. It affects whether a model and its active working state fit in a GPU’s memory.
- Bandwidth is how quickly data can move between HBM and the GPU. Higher bandwidth can help keep compute units supplied with data.
More capacity does not automatically mean more bandwidth, and neither number alone predicts application speed. Compute throughput, cache behavior, communication between GPUs, software, and the workload all matter. Vendor bandwidth specifications are not end-to-end performance benchmarks.
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How Micron’s HBM connects to NVIDIA platforms
Micron manufactures memory stacks, while NVIDIA designs GPU platforms and integrates HBM in a GPU package. Micron’s February 2024 announcement said its 24 GB, 8-high HBM3E was part of NVIDIA H200 GPUs. In March 2025, Micron named its 36 GB, 12-high HBM3E for NVIDIA HGX B300 NVL16 and GB300 NVL72, and said its 24 GB, 8-high HBM3E was available for HGX B200 and GB200 NVL72. These statements establish links to those named products, not to every NVIDIA GPU. See Micron’s 2024 HBM3E announcement and 2025 NVIDIA platform announcement.
Micron HBM3E configurations
Micron lists its 8-high HBM3E at 24 GB and its 12-high version at 36 GB, each with over 1.2 TB/s per placement on its HBM3E product page. Micron also claimed in 2024 that its 24 GB 8-high product offered about 30% lower power than competing HBM3E offerings; that is Micron’s comparative claim, not an independent benchmark.
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How HBM specifications vary across NVIDIA GPUs
The following vendor-published figures show why memory capacity and bandwidth should be compared for a specific platform and GPU configuration. They are specifications, not controlled performance comparisons.
| GPU or platform | HBM capacity per GPU | Memory bandwidth per GPU | Source context |
|---|---|---|---|
| NVIDIA A100 | 80 GB HBM2 | Up to 2,039 GB/s | NVIDIA documentation; publication date not stated on the page |
| NVIDIA H100 SXM5 | 80 GB HBM3 across five stacks | Over 3 TB/s | NVIDIA Hopper architecture article; publication date not stated on the page |
| NVIDIA HGX H100 SXM | 80 GB HBM3 | 3.35 TB/s | NVIDIA HGX component reference, current at research time |
| NVIDIA HGX H200 SXM | 141 GB HBM3e | 4.8 TB/s | NVIDIA HGX component reference, current at research time |
| NVIDIA HGX B200 SXM | 180 GB HBM3e | Up to 8 TB/s | NVIDIA HGX component reference, current at research time |
Sources: NVIDIA’s GPU Performance Background User’s Guide, Hopper Architecture In-Depth, and HGX AI Factory component specifications.
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What these specifications mean for data-center workloads
A larger HBM capacity can allow more model data or active state to reside on one GPU; bandwidth indicates how quickly that memory can serve the GPU. But comparing accelerators for a real deployment requires more than these two figures. Compute capability and precision, inter-GPU and host interconnects, power and cooling limits, software, and the target workload can all affect results. Use measured results for the workload and systems being considered rather than treating a bandwidth figure as a speedup estimate.
HBM is integrated into the data-center GPU package, not a practical consumer upgrade component. NVIDIA platforms also differ in their memory configurations, so a Micron supply announcement for one product family should not be generalized to another.
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