The Engine of the AI Revolution: What is High Bandwidth Memory (HBM) and How Does It Work?
An in-depth exploration of the architecture and future of HBM technology, which provides ultra-fast data throughput for artificial intelligence supercomputers.
In today's era of Artificial Intelligence (AI), when we interact with ChatGPT or other large language models, thousands of powerful processors are working behind the scenes. These processors require not only computational power but also the ability to access data at extreme speeds. The RAM used in standard computers fails to provide this level of speed, and this is where High Bandwidth Memory (HBM) comes into the picture. HBM is not just a memory chip; it is a remarkable example of semiconductor engineering that delivers data to the processor at lightning speed.
According to Wikipedia and industry reports, HBM is an interface for 3D-stacked synchronous dynamic random-access memory (SDRAM). Its development was initiated jointly by Samsung, AMD, and SK Hynix. The primary objective of this technology was to increase the data transfer capacity, or bandwidth, in graphics accelerators, network devices, and supercomputers. The first HBM chip was produced by SK Hynix in 2013, and it became available in the market with the AMD Fiji GPU in 2015. Since then, HBM has established an indispensable position in the field of AI.
3D Stacking: The Modern Architecture of Memory
The most significant feature of HBM is its structure. Traditional memory chips are arranged flat on the motherboard, which occupies significant space and requires data to travel a long distance to reach the processor. HBM solves this problem through 'stacking.' In this, multiple memory dies, or small chips, are arranged on top of each other, creating a structure like a multi-story building. A single stack can contain up to 32 DRAM dies. Due to this structure, it can accommodate more memory in less space and also reduces power consumption.
To connect these layers, through-silicon vias (TSVs) and microbumps are used. TSVs are ultra-microscopic holes drilled into the silicon chip that transport data vertically. This method provides a very short path for data to travel. At the bottom of the stack is a base die that contains buffer circuitry and test logic, which manages the operation of the entire stack. Because of this architecture, HBM is many times faster than other memory despite being much smaller.
- DRAM die productionFirst, multiple independent DRAM memory chips (dies) are prepared on a silicon wafer.
- TSV drillingThousands of microscopic holes (Through-Silicon Vias) are drilled into each chip and filled with conductive metal.
- Vertical stacking8 to 16 memory chips are stacked on top of each other and connected vertically via microbumps.
- Interposer connectionThe prepared memory stack is placed on a silicon interposer very close to the processor.
Interposer and the Data Highway
Another reason for HBM's incredible speed is its bus width. Bus width refers to the capacity of how many data bits can travel simultaneously. In standard GDDR memory, this width is 32 bits, whereas a single stack of HBM1 has a width of 1024 bits. If a graphics card has four such stacks, the total bus width reaches up to 4096 bits. It is precisely because of this massive data highway that AI models can process billions of parameters in a fraction of a second.
Due to the large number of connections, a special method is required to connect HBM to the processor. Companies like AMD and Nvidia use a semiconductor device called an interposer for this purpose. An interposer is a silicon layer upon which the GPU and HBM stacks are placed in very close proximity. This shortens the data paths and increases speed. However, this method is much more expensive than standard circuit board manufacturing, which is a primary reason for the high cost of HBM-based graphics cards.
| Generation | Bus Width (bits) | Maximum Capacity | Pin Speed |
|---|---|---|---|
| HBM1 | 1024 | 4 GB | 1 GT/s |
| HBM2 | 1024 | 8 GB | 2 GT/s |
| HBM3 | 1024 | 24 GB | 6.4 Gb/s |
| HBM3E | 1024 | 36 GB | 9.6 Gb/s |
| HBM4 | 2048 | 64 GB | 8 Gb/s |
| Generation | Bandwidth (per stack) | Capacity |
|---|---|---|
| HBM1 | 128 GB/s | 4 GB |
| HBM2 | 256 GB/s | 8 GB |
| HBM3 | 819 GB/s | 24 GB |
| HBM3E | 1.2 TB/s | 36 GB |
| HBM4 | 2 TB/s | 64 GB |
Progress generation by generation: From HBM1 to HBM4
HBM technology has been continuously evolving. The first-generation HBM1 could transfer 128 GB of data per second and had a capacity of 4 GB per package. HBM2, which arrived in 2016, doubled this speed to 256 GB/s and increased the capacity to 8 GB. Subsequently, HBM2E was introduced, in which stacking up to 12 layers became possible and the capacity reached 24 GB. Companies like Samsung and SK Hynix have continuously competed in this field to increase data transfer rates.
In the year 2022, the HBM3 standard was announced by JEDEC, which proved to be a game-changer for the AI revolution. In HBM3, the number of data channels was increased from 8 to 16. According to SK Hynix, their HBM3 memory provides a bandwidth of up to 819 GB/s. This same memory is used in Nvidia's famous H100 GPU. Following this came HBM3E, which possesses a speed of 1.2 TB/s and a capacity of up to 36 GB. The company Micron announced the start of mass production of HBM3E in 2024, which will be used in the Blackwell series of GPUs.
Market status and future planning
The years 2025 and 2026 have seen an unprecedented surge in the demand for HBM. Due to this demand from the AI sector, the prices of standard DRAM memory have increased by more than 200%. According to Micron, the production of HBM requires three times the capacity compared to standard DDR5 memory, which has created a memory shortage in the market. Currently, SK Hynix, Samsung, and Micron are the key players in this sector, while TSMC performs the critical task of manufacturing the base die for this memory.
Looking to the future, the HBM4 standard was announced in April 2025. In HBM4, the bus width will be increased from 1024 to 2048 bits, which will provide a bandwidth of 2 TB/s per stack. Furthermore, Samsung has also begun work on technologies like HBM-PIM (Processing-in-memory), which will incorporate a small AI engine within the memory itself. This will reduce the need to send data to the processor and make computations faster. As AI models become more complex, HBM technology will continue to play its role as the cornerstone of this digital revolution.
