HBM4: How High Bandwidth Memory Works in AI

HBM4 is the fourth generation of High Bandwidth Memory, a specialized type of DRAM designed to sit close to a processor and feed it data extremely fast. In AI workloads, where models constantly shuffle massive matrices and tensors, memory bandwidth often matters more than raw compute speed. This article explains what HBM4 is, how it achieves its speed, and why it is becoming the standard memory for AI accelerators.

Formula

The bandwidth of any memory system is calculated by multiplying the data rate (transfers per second) by the bus width (bits transferred per transfer) and then converting bits to bytes. For HBM, the data rate is doubled because data is transferred on both the rising and falling edges of the clock signal (double data rate, DDR).

Bandwidth (GB/s) = (Bus Width (bits) × Data Rate (MT/s) × 2) / 8 / 1000

Where:

• Bus Width (w): the total number of data lines between the memory and the processor. For HBM4, this is typically 8192 bits (8 stacks × 1024 bits per stack).
• Data Rate (d): the number of data transfers per second per pin, measured in MT/s (megatransfers per second). HBM4 is expected to reach 6400 MT/s or higher.
• 2: the DDR factor, since data is transferred on both clock edges.
• /8: converts bits to bytes.
• /1000: converts bytes to kilobytes (using decimal, as is standard in storage and memory marketing).

Worked Example

Using the default values from the calculator (Memory clock = 2000 MHz, Bus width = 8192 bits, Data rate = 6400 MT/s):

1. Multiply the data rate by the bus width: 6400 × 8192 = 52,428,800 (megabits per second).
2. Multiply by 2 for DDR: 52,428,800 × 2 = 104,857,600 (megabits per second).
3. Divide by 8 to get megabytes: 104,857,600 / 8 = 13,107,200 (megabytes per second).
4. Divide by 1000 to get gigabytes: 13,107,200 / 1000 = 13,107.2 GB/s.

The calculator shows 13107.2 GB/s, which is approximately 13.1 TB/s. This matches the expected bandwidth of HBM4.

Background: Why AI Needs HBM

AI models like large language models (LLMs) and transformers are memory-bound. During inference, every token generated requires reading the entire model weights from memory. For a 70-billion-parameter model, that’s roughly 140 GB of weights (at 16-bit precision). To generate tokens at a reasonable speed, the memory must deliver these weights faster than the GPU can compute. Traditional GDDR memory, while fast, has a limited bus width and requires significant power. HBM solves this by stacking DRAM dies vertically and connecting them to the processor via a wide, short bus. This reduces the distance data must travel, cutting latency and power consumption while dramatically increasing bandwidth.

How HBM4 Works

HBM4 is built on a 3D stacking architecture. Multiple DRAM dies (typically 16 per stack) are stacked vertically and connected through silicon vias (TSVs). These TSVs are tiny vertical wires that pass through each die, allowing signals to travel between layers. The entire stack is then placed on a silicon interposer—a large, thin piece of silicon that also holds the GPU or AI accelerator. The interposer provides the wide, dense wiring needed to connect the HBM stacks to the processor.

Each HBM4 stack has a 1024-bit interface (compared to 512-bit for HBM3), and with 8 stacks, the total bus width is 8192 bits. The data rate is expected to be 6400 MT/s, yielding over 13 TB/s of bandwidth per stack group. HBM4 also introduces a lower operating voltage (1.0V compared to 1.1V for HBM3), which reduces power consumption by about 20% for the same bandwidth.

How to Choose the Right HBM Configuration

When selecting an AI accelerator, the HBM capacity and bandwidth are key specifications. The required capacity depends on the model size and batch size. For a 70B parameter model with 16-bit weights, you need at least 140 GB of memory. HBM4 stacks are expected to offer capacities up to 48 GB per stack, so a 4-stack configuration would give 192 GB, enough for the model plus activations.

Bandwidth determines the maximum tokens per second you can generate. A rough rule: tokens per second ≈ bandwidth / (model size in bytes). For a 140 GB model and 13 TB/s bandwidth, that’s about 93 tokens per second, which is fast enough for real-time chat. If you need higher throughput, you might choose a configuration with more stacks or a higher data rate.

Common Problems and Misconceptions

Misconception: HBM4 is just faster DDR. HBM is fundamentally different—it’s a 3D stacked memory with a wide interface, not a traditional DIMM. You can’t upgrade a system by swapping memory; the HBM is soldered onto the interposer alongside the processor.

Problem: Heat dissipation. Stacking dies generates more heat per cubic millimeter. HBM4 addresses this with improved thermal management, but cooling is still a critical design consideration. Data centers using HBM4 accelerators need robust liquid cooling or advanced air cooling to maintain performance.

Problem: Yield and cost. Manufacturing 3D stacked memory with TSVs is complex, leading to lower yields and higher costs compared to planar DRAM. This is why HBM is reserved for high-end AI accelerators and not consumer PCs.

Comparison: HBM4 vs. HBM3 vs. GDDR7

HBM4 offers roughly double the bandwidth of HBM3 (13 TB/s vs. 6.4 TB/s) and a 25% reduction in power per bit. GDDR7, used in gaming GPUs, has a much narrower bus (typically 256-bit) and tops out around 1.5 TB/s for a high-end card. HBM4 is superior for AI training and inference, but GDDR7 is cheaper and sufficient for consumer graphics. The choice depends on the workload: HBM for data-center AI, GDDR for gaming and entry-level compute.

Cost and Availability

HBM4 is expected to enter mass production in late 2025 or 2026. The cost per gigabyte is significantly higher than GDDR or standard DDR—roughly 3-5 times more. This is due to the complex manufacturing process and the need for a silicon interposer. For AI companies, the cost is justified by the performance gains, but it means HBM4 will initially be limited to premium AI accelerators from companies like NVIDIA, AMD, and Intel. Smaller players may opt for HBM3 or alternative memory solutions to reduce cost.

Frequently Asked Questions

What is the difference between HBM and GDDR? HBM is stacked vertically and connected via a wide bus (1024 bits per stack), while GDDR is planar and uses a narrower bus (typically 32-64 bits per chip). HBM offers much higher bandwidth per watt but is more expensive and limited to high-end accelerators.

Can I use HBM4 in my gaming PC? No. HBM4 is designed for data-center AI accelerators and is not compatible with consumer motherboards. Gaming GPUs use GDDR memory, which is cheaper and sufficient for gaming workloads.

Will HBM4 make AI models run faster? Yes, but only if the model is memory-bound. For large models, HBM4’s increased bandwidth directly reduces the time to read weights, speeding up inference and training. For small models, the compute may be the bottleneck, so the benefit is less pronounced.

How does HBM4 affect power consumption? HBM4 operates at a lower voltage (1.0V) and has a higher bandwidth per watt than HBM3. This means for the same bandwidth, it consumes about 20% less power, which is crucial for data-center energy costs.

Is HBM4 backward compatible with HBM3? No. HBM4 has a different interface and pinout. However, the silicon interposer can be designed to support both, but the memory controller must be updated. In practice, each generation requires a new processor design.

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in t | Memory clock (MHz) | MHz | 2000 | 1 | 10000
in w | Bus width (bits) | bits | 8192 | 64 | 32768
in d | Data rate (MT/s) | MT/s | 6400 | 1 | 20000
out bw | Bandwidth (GB/s) | GB/s | (w * d * 2) / 8 / 1000 | 1