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Micron (MU) could become more important than Nvidia for AI inference because…

2026-06-28 · 07:52 UTC ·@jukan05 ·1 min read

Brief

Micron (MU) is positioned as potentially more critical than Nvidia for AI inference: @jukan05 (2026-06-28) argues inference ROI hinges on memory capacity, not GPU count, because memory bottlenecks leave GPUs idle. A reply from @TicTocTick predicts MU falling from "now 1200" to 700 and repeats the slogan "RAM is NOT GPU."

Why it matters

Micron (MU) could become more important than Nvidia for AI inference because inference ROI depends more on memory than on additional GPUs, per @jukan05 (2026-06-28).

Key details

  • Inference workloads often leave GPUs underutilized due to memory bottlenecks, so adding memory (not more Nvidia GPUs) yields greater inference performance/value.
  • A reply from @TicTocTick predicts MU will crash from "now 1200" to 700 soon (recalling a past level of 80) and bluntly states "RAM is NOT GPU."
Source evidence

Yes, MU is not Nvidia.

But going forward, it may become even more important than Nvidia.

Think about it. Inference is now directly tied to money. But inference does not get better simply by adding more Nvidia GPUs. In fact, GPUs are often underutilized in inference, sitting idle due to memory bottlenecks.

For inference, adding more memory is far more valuable.

Ultimately, the ROI of inference depends less on GPUs and more on memory. So why are people still looking at Micron through Nvidia’s framework?

Think bigger.

Inference is memory.

tic toc tic (@TicTocTick)

MU is not NVDA!!

MU goon crash to 700 soon (now 1200).

Remember we had this at 80!!!

RAM is NOT GPU fools !!!

— https://nitter.net/TicTocTick/status/2070918228327460907#m