Serenity
2026.03.25 17:47

Google's TurboQuant...

And it's effect on $Sandisk(SNDK.US), $Micron Tech(MU.US), SK Hynix, and others:

What it does:

-> 6x reduction in KV cache memory footprint

-> 8x Speedup on H100 GPUs

It's a compression algorithm.

Now... Will it beat down memory?

-> Prob not.

Implications might be bullish for $Arm(ARM.US) and others though where you can run AI locally, rather than DRAM heavy DCs.

However:

->This is basically DeepSeek round 3. You can make algorithms more efficient. But that doesn't replace either memory or GPUs.

-> It could structurally (and slightly) reduce DRAM demand.

-> think it's only been tested on small models so far like Gemma, Mistral, and Llama-3.1 (and paper's been out for a year)

Also, markets conflated DRAM with NAND... this algo compresses the KV cache (DRAM). Doesn't do anything to NAND storage?

Regardless:

Algorithms will always get more efficient. People keep saying Jevons Paradox, which is true since this just scales use cases.

Main thing to look out for is hyperscaper capex projections, not Google Algorithms that made things more efficient.

Feels more like a narrative headwind than anything material to earnings.

Source: Serenity

  • 0

The copyright of this article belongs to the original author/organization.

The views expressed herein are solely those of the author and do not reflect the stance of the platform. The content is intended for investment reference purposes only and shall not be considered as investment advice. Please contact us if you have any questions or suggestions regarding the content services provided by the platform.