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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11HBM (high-bandwidth memory) is specialized DRAM packaged close to an AI accelerator’s processor. Its wide, fast interface helps move data to and from the compute cores, while its capacity determines how much data can stay in that nearby memory. Those are separate constraints: more capacity does not automatically mean more bandwidth, and more bandwidth does not guarantee a matching increase in overall performance.
What is HBM memory?
High-bandwidth memory, or HBM, is DRAM designed to sit in the same advanced package as a GPU or other accelerator. It is not a removable memory module like the DIMMs used for a desktop PC. HBM stacks DRAM dies and connects them through a wide interface, placing substantial memory bandwidth physically close to the processor. NVIDIA describes HBM as part of its accelerator products and lists the memory specifications for its HGX systems in its HGX reference documentation.
Two specifications matter, but answer different questions:
- Capacity, usually expressed in gigabytes (GB), is how much data the GPU’s local memory can hold.
- Bandwidth, expressed here in terabytes per second (TB/s), is the rate at which data can be transferred between that memory and the GPU.
A GPU’s local HBM bandwidth is also different from the bandwidth of links between GPUs. NVIDIA’s HGX documentation lists GPU-to-GPU interconnect figures separately; those should not be mistaken for the HBM bandwidth of a single GPU.
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Why do AI accelerators use HBM?
AI workloads move large quantities of data between memory and compute. Keeping memory close to the processor and using a wide interface provides high local bandwidth, which can help keep compute units supplied with data when a workload is limited by memory traffic.
Capacity addresses a different bottleneck: whether the data a workload needs can fit in local memory. For a large language model, that data can include model weights and inference runtime state such as the key-value (KV) cache. More capacity can allow more of the model or runtime state to remain in the GPU’s local memory. If the workload instead spends time waiting for data transfers, greater bandwidth may help feed the compute more quickly.
Neither specification alone predicts performance. Compute capability, software, parallelism, communication between GPUs, and the shape of the workload also matter. A system with multiple GPUs may have substantial memory in total, but that total does not by itself establish that the memory is shared as one pool; how GPUs communicate and distribute work matters too.
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How do HBM specifications vary by accelerator?
HBM capacity and bandwidth are product-specific. NVIDIA’s HGX reference documentation, accessed in 2026, lists these specifications for the named GPUs:
| GPU | HBM generation | Memory capacity per GPU | Local HBM bandwidth per GPU |
|---|---|---|---|
| NVIDIA H100 | HBM3 | 80 GB | 3.35 TB/s |
| NVIDIA H200 | HBM3e | 141 GB | 4.8 TB/s |
| NVIDIA B200 | HBM3e | 180 GB | Up to 8 TB/s |
These are vendor specifications for the listed products, not universal figures for every accelerator using the same HBM generation. Check the current product documentation when comparing systems because specifications can change. The B200 figure is stated as “up to” 8 TB/s.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why can HBM supply be tight?
HBM depends on specialized production and packaging rather than simply being ordinary DRAM installed in a different slot. In investor materials, SK hynix describes HBM as in-package memory for GPUs and accelerators and identifies through-silicon-via (TSV) process capacity as necessary for supplying high-density memory (SK hynix investor materials). That specialized chain means HBM output is not instantly interchangeable with all other memory production.
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Demand planning is another part of the picture. Micron said its HBM supply for calendar 2024 was sold out and that most of its 2025 supply had been allocated. In later investor materials, it described strong demand for 2026 supply and discussions with customers about agreements for that year (Micron investor materials). Separately, SK hynix and NVIDIA announced a multi-year partnership to co-develop next-generation memory and secure supply for AI infrastructure (SK hynix news releases).
These are dated company statements and strategic plans, not a current measure of an industry-wide shortage. They show that suppliers and customers were planning around strong demand in the specified periods; they do not establish the exact size of any shortfall across the industry or current spot-market availability in October 2026.
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Can I upgrade a GPU with more HBM?
No—not as a normal PC memory upgrade. HBM is integrated into the accelerator package, so it is not a user-installable DIMM or a typical add-in upgrade. If a workload needs more local memory, the practical choice is an accelerator or system configured with sufficient capacity, while also checking bandwidth and the system’s ability to communicate across GPUs.
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