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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 interface provides high local memory bandwidth, while its capacity determines how much model data and runtime state can stay near the processor. Those are separate constraints: more capacity does not automatically mean faster data transfer, and more bandwidth does not guarantee a proportional speedup.
What is HBM memory?
HBM is a type of DRAM designed to sit in the same advanced package as a GPU or other accelerator, rather than on a removable memory module elsewhere in a computer. Stacked DRAM dies connect through a wide interface to the processor, helping move data at high throughput while keeping memory physically close to compute. NVIDIA’s product documentation describes HBM as the local memory used by its accelerators, including H200’s 141 GB of HBM3e (NVIDIA H200 specifications).
The two key specifications answer different questions:
- Capacity is how much data the GPU’s local memory can hold, commonly expressed in gigabytes (GB). It affects how much of a model, its activations, and runtime state can reside on the GPU.
- Bandwidth is how quickly that memory can transfer data, commonly expressed in terabytes per second (TB/s). It matters when a workload needs to move data to and from the processor quickly.
Why do AI accelerators use HBM?
AI workloads move large amounts of data between memory and compute. HBM’s high bandwidth helps feed the processor without relying solely on more distant system memory. Its in-package placement and wide interface are part of how accelerators provide substantial local memory throughput.
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Capacity matters alongside bandwidth. Large language model weights occupy memory, and inference also uses runtime state such as the key-value (KV) cache. More capacity can let a GPU keep more weights or state locally. More bandwidth can move data faster when memory traffic is the bottleneck. Neither specification alone determines performance: compute capability, software, parallelism, communication between GPUs, and workload shape also affect results.
How much HBM do AI accelerators have?
Specifications vary by product. NVIDIA’s HGX reference documentation lists these examples:
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| GPU | HBM capacity per GPU | HBM type | Memory bandwidth per GPU |
|---|---|---|---|
| H100 | 80 GB | HBM3 | 3.35 TB/s |
| H200 | 141 GB | HBM3e | 4.8 TB/s |
| B200 | 180 GB | HBM3e | Up to 8 TB/s |
These are NVIDIA-published specifications for the named GPUs, not universal figures for HBM-equipped accelerators. The B200 bandwidth is stated as “up to” 8 TB/s. See the NVIDIA HGX reference documentation for system details. Its GPU-to-GPU interconnect figures describe communication between GPUs; they are not the same as one GPU’s local HBM bandwidth.
When comparing accelerators, check capacity and bandwidth per GPU separately, then consider how many GPUs a system has and how they communicate. A system’s aggregate memory capacity is not automatically a single shared pool; whether GPUs can access one another’s memory, and how efficiently, depends on the system and software.
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Why can HBM supply be tight?
HBM production involves specialized memory and packaging processes. SK hynix describes HBM as memory integrated into GPU and accelerator packages and identifies through-silicon-via (TSV) process capacity as necessary for high-density supply in its 2023 financial results materials. That specialized chain means HBM output is not simply interchangeable with ordinary DRAM production.
Demand planning is another factor. Micron said its HBM supply was sold out for calendar 2024 and that most of its 2025 supply had been allocated, according to its investor materials. In later materials, the company described strong demand for 2026 HBM supply and discussions with customers about agreements for that year (Micron investor materials). SK hynix and NVIDIA also announced a multi-year partnership to co-develop next-generation memory and support AI infrastructure supply (NVIDIA and SK hynix partnership announcement).
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These disclosures show supplier allocation and advance planning during the periods they describe; they do not establish an exact industry-wide shortfall or current spot-market availability in October 2026. They also do not quantify how much of any present tightness comes from DRAM production, packaging capacity, yields, or customer allocation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can I upgrade a GPU with more HBM?
No—not as a normal PC memory upgrade. HBM is integrated into the accelerator package, not installed as a user-replaceable DIMM or graphics-memory module. To get more HBM, a buyer generally needs an accelerator model or system built with a higher-capacity configuration. Check the specific product’s vendor documentation rather than assuming that all accelerators in a family have identical memory.
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