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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →HBM2 and GDDR6 are both graphics-memory technologies, but they connect to GPUs in different ways. HBM2 stacks memory dies beside the GPU on the same package, while GDDR6 uses separate memory components connected through the GPU’s memory interface. That design affects how manufacturers build a memory subsystem; it does not, by itself, determine which GPU is faster or more power-efficient.
How HBM2 and GDDR6 are built and placed
HBM2: stacked dies on the GPU package
High Bandwidth Memory 2 (HBM2) uses stacks of memory dies placed on the same physical package as the GPU. NVIDIA attributes power and area savings, compared with traditional GDDR5/6 designs, to this close placement and the associated memory connections. Those are implementation benefits, not a guarantee of lower total-system power for every GPU. NVIDIA’s Volta white paper describes the V100 configuration as four stacks, each containing four memory dies, with up to 16 GB of GPU memory.
GDDR6: graphics DRAM connected across the system
GDDR6 is high-speed DRAM designed for graphics and other high-bandwidth applications. Its memory components are placed around the GPU as part of the graphics system, rather than stacked beside it on the same package. Micron’s example configuration uses 12 placements and a 384-bit I/O interface; Samsung describes GDDR6 as specialized for high-bandwidth graphics processing and parallel workloads. Actual placement and interface details vary by GPU. Micron’s graphics-memory overview and Samsung’s GDDR6 page describe these respective technologies.
Bus width, per-pin speed, and total bandwidth
These terms describe related but different things. The memory interface, often described by its bus width, is the data path connecting memory and GPU. Per-pin transfer rate describes how quickly data is transferred on each data connection. Aggregate bandwidth combines the interface width with the data rate, so a per-pin figure alone does not tell you how much data the complete GPU memory subsystem can move.
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For that reason, compare bandwidth figures only with their full GPU or system configuration attached. A wider interface can support more aggregate bandwidth at a given transfer rate, but memory bandwidth is not the same as application speed: cache behavior, workload, GPU design, and other factors also affect performance.
What the published bandwidth examples show
The figures below illustrate different vendor-published configurations. They are not results from a controlled HBM2-versus-GDDR6 test, and they cannot establish relative frame rates, application performance, or energy use.
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| Example | Memory configuration | Capacity | Published bandwidth |
|---|---|---|---|
| NVIDIA Tesla V100 (2017) | HBM2, four stacks | Up to 16 GB in NVIDIA’s Volta description | 900 GB/s peak, according to NVIDIA |
| NVIDIA A100 (2020) | HBM2, five active stacks | Not stated in the cited bandwidth source | 1,555 GB/s, according to NVIDIA |
| Micron HBM2 product information (accessed 2026) | HBM2 | 4 GB, 8 GB, and 16 GB options stated by Micron | Up to 410 GB/s, according to Micron |
| Micron 12-placement GDDR6 example (accessed 2026) | GDDR6, 12 placements; 384 I/O in the example | Not stated in the cited comparison | 672–960 GB/s at 14–20 Gb/s per pin, according to Micron |
The values come from different GPUs, configurations, and publication contexts, so they should not be ranked as though they were measured under identical conditions. Micron’s HBM2 product figure is a product-information maximum, while the NVIDIA figures describe specific GPU implementations; Micron’s GDDR6 range is for its stated 12-placement example.
Why different graphics systems use each type
HBM’s short connections can support energy-efficient communication near a GPU, a useful design consideration in high-performance systems. The 2025 IEA 4E report describes HBM as increasingly used in high-performance data-center GPUs. It also notes that consumer gaming GPUs commonly use GDDR with large caches, which can reduce the need for extremely high external-memory bandwidth. These are broad segment trends, not rules that every product follows. The IEA 4E server-efficiency report discusses memory technologies in this system context.
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Memory choice is one part of a larger design trade-off. Workload, total memory capacity, cache behavior, package design, power budget, and price all matter. A bandwidth number alone does not reveal how a GPU performs on a particular game, compute task, or other application.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to interpret GDDR6 efficiency claims
In a 2022 announcement, Samsung said it was sampling 24 Gbps GDDR6 and described approximately 20% higher power efficiency at 1.1 V for certain 20 and 16 Gbps low-power versions compared with 1.35 V industry-standard GDDR6. These are Samsung’s claims for specified variants, not independent measurements or a general efficiency result for all GDDR6 memory. Samsung’s announcement also reflects the company’s view at the time about growing demand for graphics capabilities.
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