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北京时间·🕒 发布 03-26 01:47:01时区推断·⟳ 抓取 06-03 23:04:54回填·⚡ 决策 06-17 23:56:02·131.8 天前
KOL讨论Google TurboQuant算法对存储股影响，认为系旧闻且无实质利空
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Google's TurboQuant... And it's effect on $SNDK, $MU, SK Hynix, and others: What it does: -> 6x reduction in KV cache m…
不做 SNDK.US
SanDisk(SNDK)已于2016年被西部数据(WDC)收购并退市，该代码无法交易，作者可能误用旧代码代指NAND存储板块。
不做 MU.US
帖子讨论的是已发布一年的旧算法(TurboQuant)，且KOL自身结论为对盈利无实质影响、仅为叙事逆风。属于旧闻与情绪噪音，无新催化剂，不具备交易价值。
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Google's TurboQuant...
And it's effect on $SNDK, $MU, 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 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.
And it's effect on $SNDK, $MU, 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 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.
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