In the ever-evolving world of artificial intelligence (AI), performance is everything. As researchers and engineers push the boundaries of what machines can learn and accomplish, the underlying hardware becomes increasingly important. At the heart of this hardware lies memory—and more specifically, memory bandwidth.
You might be surprised to learn that the speed at which a processor can access and move data has a massive impact on how quickly and efficiently AI workloads are handled. In this blog post, we’ll unpack two major types of memory technologies used in AI systems today—HBM2e (High Bandwidth Memory 2 Enhanced) and GDDR6 (Graphics Double Data Rate 6)—and explore why memory bandwidth matters so much in AI workloads. We’ll use real-world examples, industry insights, and visual breakdowns to help you understand these technologies and their applications.
Think of memory bandwidth like a highway between your CPU or GPU and your memory modules. The wider the road and the faster the cars can move, the more data gets transferred in less time. For AI, where workloads often include large-scale models and massive datasets, this highway needs to be as wide and fast as possible.
Memory bandwidth is measured in gigabytes per second (GB/s), and a higher bandwidth ensures that processors aren’t left idling while waiting for data to arrive. In AI applications, where milliseconds matter, this difference can significantly affect everything from training time to inference speed.
Let’s take a closer look at the two memory technologies we’re comparing.
HBM2e (High Bandwidth Memory 2 Enhanced)
GDDR6 (Graphics Double Data Rate 6)
Let’s step into the shoes of an AI engineer. You’re training a deep learning model with millions (or even billions) of parameters. Each training step requires accessing huge amounts of data, performing matrix operations, and storing intermediate results. This cycle is repeated millions of times.
If your memory bandwidth is too low, your processor ends up waiting. A powerful GPU won’t do much good if it’s sitting idle because the memory can’t keep up. It’s like owning a Ferrari but only being able to drive it on a dirt road.
Training
Training large-scale models, such as GPT or BERT, can take days or even weeks. High memory bandwidth reduces the time it takes to feed data into compute units, dramatically shortening the training process.
Inference
Inference might seem simpler, but it’s just as sensitive to latency and throughput—especially in real-time applications like autonomous driving, voice assistants, or financial trading systems.
HBM2e in High-End AI Systems
Several leading AI hardware platforms leverage HBM2e for its unmatched bandwidth and efficiency:
These platforms are built for environments where performance and efficiency are paramount—like data centers and supercomputers.
GDDR6 in Mainstream Solutions
GDDR6 continues to dominate in the consumer and prosumer space:
GDDR6 strikes a balance between affordability, availability, and performance—making it suitable for small-scale AI models, educational use, and developers testing proofs of concept.
HBM3 and GDDR7 on the Horizon
These future standards aim to keep up with the relentless pace of AI innovation.
Software Optimization
No matter how fast the memory is, poor software optimization can nullify its benefits. Techniques such as:
...can all improve how memory bandwidth is utilized.
Domain-Specific Hardware
We’re also seeing a trend toward domain-specific accelerators like Google’s TPUs and Graphcore IPUs. These designs often prioritize memory bandwidth as a core architectural feature to meet the growing demands of AI workloads.
There’s no one-size-fits-all solution. Here's a quick guide to help you decide:
Go with HBM2e if:
Opt for GDDR6 if:
AI is revolutionizing industries, from healthcare to finance to entertainment. Whether you’re developing cutting-edge language models or building smarter recommendation engines, understanding the role of memory bandwidth—and how HBM2e and GDDR6 compare—can help you make better technology choices.
