The power unit of artificial intelligence is memory. It would otherwise be a failure for even the smartest models. Micron Technology creates that engine stealthily and accurately, at a size most people wouldn’t think of until it breaks.
How to tell what Micron actually does
Micron is a manufacturer of memorizing and storing semiconductors. It was established in 1978 in Boise, Idaho, and is making chips for DRAM, NAND flash and NOR flash, which are found in smartphones, data centers, laptops, automotive systems and industrial equipment around the world. Imagine every piece of equipment that is used to store or manipulate information Micron’s components are probably hidden in the device. DRAM is for currently processing data. NAND is used to store data that will last for a long time. With two product lines bringing in billions in revenue each year, Micron is one of three top DRAM manufacturers in the world, along with Samsung and SK Hynix.
AI in the Picture: Where
AI models have an enormous memory requirement. The key takeaway is that training a large language model demands the simultaneous processing of huge amounts of data, involving millions or even billions of parameters that are constantly being transferred between the processing units and memory chips. Such bottlenecks can never be overcome by any up-gradation of a processor, because they stem from slow memory. Those are all completely eliminated with fast, high band-width memory.
Micron’s HBM3E High Bandwidth Memory provides all this. The first of its kind aimed at improving AI infrastructure, HBM3E mounts memory chips one above the other, drastically reducing the distance data must cover. Result? 1.2 Tera bytes per second per stack. For models such as GPT or Gemini, memory bandwidth plays a significant role in the learning speed, making that number crucial.
The Data Center Transformation
The chips from the company NVIDIA, the largest player in the dominant global AI training market, are powered by Micron’s HBM3E. Between 2022 and 2024, data centers that use AI workloads grew their memory by twofold. Revenue from data center products at Micron also surged as hyperscaler such as Microsoft, Google and Amazon have ramped up their AI infrastructure expansion by leaps and bounds. Having said that, there is a second crucial component to AI, that of inference, which is often over looked in training.
Training gets attention. The amount of Inference running the AI models with real userss gets reduced. Inference occurs many billions of times a day on every AI application. The data for each inference request is constantly being pulled through memory chips.
Automotive and Industrial AI
Self-Driving systems are real-time processing systems that operate with sensor data. On the manufacturing floor, industrial robots make quick decisions. They both need memory that is highly stable over a wide temperature range and in the face of physical stresses. This is exactly the specification that micron’s chips for the automotive market adhere to and consumer memory chips do not.
Conclusion
The memory needed for AI is expanding even more quickly than many have anticipated. To meet the competition of buyers, Micron has made a $150b investment in new manufacturing by 2030. All advances in AI whether in model design, edge AI devices or autonomous systems pass through memory architecture that Micron is actively forging.