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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →HBM is a family of high-bandwidth memory used in AI and high-performance computing accelerators. HBM3E raises per-stack bandwidth beyond 1.2 TB/s in Micron’s specification, while HBM4 redesigns the interface around 2,048 I/O pins and supplier-reported maxima above 2.8 TB/s. Those are product claims, not guaranteed system speeds: buyers also need to compare capacity, stack configuration, compatibility, power, availability and performance on their actual workload.
What HBM is—and what the generation labels mean
High-bandwidth memory (HBM) is specialized DRAM packaged close to a processor. Instead of arranging memory chips side by side on a conventional board, HBM stacks DRAM dies vertically and connects them through through-silicon vias (TSVs). Its compact package and wide interface are designed to move data at high rates. Micron describes HBM as specialized 3D-stacked SDRAM in its HBM portfolio.
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HBM is typically integrated into an AI or high-performance computing accelerator package. It complements system memory such as DDR5 or LPDDR; it is not a drop-in replacement for that memory. HBM3E and HBM4 identify successive generations, but a generation label alone does not specify a particular stack’s capacity, bandwidth or compatibility with an accelerator.
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The clearest architectural change in the supplier materials is HBM4’s wider interface: Samsung and SK hynix describe 2,048 I/O pins or terminals, twice the 1,024-pin interface they cite for the prior generation. Suppliers also report higher bandwidth per stack for HBM4. The figures below are vendor specifications for particular products, not a controlled, cross-vendor comparison or a promise of application-level performance.
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| Generation and supplier | Supplier-reported bandwidth per stack | Capacity and configuration stated | What the figure establishes |
|---|---|---|---|
| HBM3E, Micron | More than 1.2 TB/s | The reviewed portfolio page does not give one universal capacity. | Micron’s product figure; not a guarantee for every HBM3E design. Source |
| 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 claim, but the products and terms are not identical. Source |
| HBM4, Micron | More than 2.8 TB/s | 36 GB 12-high product; 48 GB 16-high customer samples in 2026. | The 48 GB figure refers to samples, not broad market availability. Source |
| HBM4, Samsung | Up to 3,300 GB/s | 36 GB 12-high on its product page; February 2026 announcement described 24–36 GB and plans for up to 48 GB with 16-high stacking. | Samsung’s product maximum; the 48 GB configuration was described as a plan in that announcement. Source |
| HBM4, SK hynix | Over 10 Gbps operating speed; the announcement says bandwidth doubled versus its previous generation. | 2,048 I/O terminals; no per-stack capacity or TB/s maximum stated in the cited announcement. | A supplier-reported speed and development milestone, not an independently measured comparison. Source |
GB/s and TB/s measure bandwidth—data transferred per second. GB measures capacity—data held. A bandwidth number for one stack is not the same as total accelerator bandwidth: the system’s stack count and package configuration matter. Vendors may also use different product conditions and units, so do not treat a single maximum as a universal speed for a generation.
How much faster is HBM4?
On the cited product pages, Micron reports more than 2.8 TB/s per HBM4 stack versus more than 1.2 TB/s for its HBM3E portfolio; Samsung reports up to 3,300 GB/s for HBM4 versus up to 1,180 GB/s for HBM3E. Both sets of figures indicate substantially higher supplier-stated per-stack bandwidth for HBM4, but they are not identical product claims or an apples-to-apples test.
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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
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Samsung’s February 12, 2026 announcement says its HBM4 delivers a consistent 11.7 Gbps, can reach up to 13 Gbps, and provides up to 3.3 TB/s per stack. It also describes the consistent speed as approximately 46% above an 8 Gbps industry standard. These are Samsung’s claims, not independent measurements. SK hynix’s September 12, 2025 announcement reports over 10 Gbps operating speed and more than 40% improved power efficiency versus its previous generation; Samsung reported a 40% power-efficiency improvement versus HBM3E. These efficiency comparisons are likewise supplier-reported and should not be read as results for every accelerator or workload.
None of these per-stack figures proves a particular increase in model throughput, lower latency or total system performance. The reviewed supplier sources do not provide a neutral, controlled HBM3E-versus-HBM4 system benchmark. Actual results depend on the accelerator, memory capacity and configuration, workload, software and the way the full system is built.
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HBM4 availability and product status
Samsung
Samsung announced HBM4 mass production and commercial shipments on February 12, 2026. Its release described 24–36 GB 12-layer options and plans for up to 48 GB using 16-layer stacking. Its product page lists a 36 GB 12-high product and up to 3,300 GB/s. These are supplier statements; they do not establish availability in a particular accelerator or region.
SK hynix
On September 12, 2025, SK hynix said it had completed HBM4 development and was prepared for mass production. It cited 2,048 I/O terminals, over 10 Gbps operating speed and more than 40% improved power efficiency versus the previous generation. A readiness announcement is not the same as confirmation of a specific accelerator’s qualification, shipment volume or delivery date.
Rank #4
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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.
- Intel Xe2-HPG Architecture: Built on Intel's next-gen architecture with 20 Xe cores and 160 XMX engines for AI acceleration (197 INT8 TOPS).
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Micron
Micron’s HBM portfolio page says HBM4 is in high-volume production. Its product information identifies a 36 GB 12-high HBM4 stack and 48 GB 16-high customer samples in 2026. Samples are not equivalent to broad availability, and a portfolio statement alone does not confirm support in a particular platform.
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Supplier milestones describe each company’s own products and schedules; they do not establish equivalent qualification or availability across suppliers. Confirm supported generation, stack configuration and delivery timing with the accelerator maker or system vendor.
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What AI hardware buyers should compare
When evaluating accelerator options, compare the complete memory configuration and the performance the system delivers—not just the generation name or a single bandwidth maximum.
- Capacity per accelerator: Check whether total HBM supports the target model, batch size, context length and workload. Capacity can constrain what fits in memory even when bandwidth is high.
- Bandwidth at the system level: Ask for aggregate bandwidth and the memory configuration behind it, including the number of stacks. A per-stack peak is not the accelerator’s complete bandwidth specification.
- Stack and package configuration: Compare capacity per stack, stack height and number of stacks, and verify that the accelerator package supports the configuration being offered.
- Compatibility and supply: Confirm the exact HBM generation and capacity supported by the specific accelerator, along with qualification and delivery schedules for that platform.
- Power and thermal behavior: Treat supplier efficiency figures as prompts for questions. Seek platform-level measurements under deployment conditions and on the planned workload.
- Workload results and total cost: Compare measured throughput, latency and utilization on the intended model and software stack, then assess those results against system cost. Supplier component specifications alone cannot answer this.
When does HBM4 make a meaningful difference?
HBM4’s wider interface and higher supplier-reported stack bandwidth may matter when the accelerator and workload can use them. But a larger peak does not by itself show that an application will run faster: the workload may be limited by capacity, compute, software or another part of the system. Buyers should ask for workload-specific evidence and verify that the compared systems have appropriate, clearly stated memory configurations.
HBM3E remains a high-bandwidth option, with supplier-listed stack figures around or above 1.2 TB/s. If a platform meets capacity and performance needs, its generation label alone is not enough to show that a more recent memory configuration is the better purchase. The comparison should be between complete, compatible accelerator systems, not isolated memory maxima.
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