The first phase of the current artificial intelligence (AI) boom was about training the basic large-scale language models (LLMs) needed to help answer questions and perform tasks. Training these AI models requires a ton of processing power, that’s where it comes in. Nvidia (NASDAQ: NVDA ) And its graphics processing units (GPUs) shine.
Not only are Nvidia’s GPUs powerful, but the company has also seeded it smartly. CUDA Software platforms in places where early AI research was being done. This led to early core code being written on its software and optimized for its chips, giving it a huge moot and making it the biggest AI winner.
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While AI model training is important, it is starting to shift to an AI-centric market. guess And Agent AI. New winners will emerge as factors such as memory, central processing units (CPUs), and cost per estimate become more important. Let’s look at three AI stocks that could become the next big AI winners.
AMD: An agentive AI and inference winner
The shift towards agentic AI and inference drives this. Advanced Micro Devices‘ (NASDAQ: AMD ) Strengths First, both require very high CPU-to-GPU ratios: training at about 1:8, inference at about 1:4, and agent AI at 1:1. AMD is the leader in the high-performance data center CPU market, so this is a huge tailwind for the company. In addition, data center CPU prices should also increase, as agent AI requires more cores (similar to individual workstations with chips), which should increase prices.
AMD’s chiplet GPUs, meanwhile, pack in more memory, which makes them ideal for inference, which is more memory-intensive than processing. With major GPU inference deals, the company is poised to become a winner in inference and agent AI.
Micron: A HBM leader
As mentioned above, inference is more memory bound than processing bound, which is great news for a dynamic random access memory (DRAM) manufacturer. Micron Technology (NASDAQ: MU ). The DRAM market is already undersupplied, causing prices to rise, and the only change in the market should be an increase in demand.
Typically, GPUs need to be packed with a special form of DRAM called high-bandwidth memory (HBM) to perform better, and inference uses even more of that memory. While static random access memory is a competing technology, it is physically large and expensive. With AI chip demand continuing to rise, the outlook for HBM and DRAM is strong. Meanwhile, the three major DRAM manufacturers — Micron and Korean companies — Samsung And SK Hanks — has begun closing long-term (three to five-year) deals, marking a major shift for the memory industry.