POET has partners with most crucial importance:
Mitsubishi, FIT (Foxconn), Luxshare: They all supply the BIG.
In 2024 POET got three awards regarding best AI solution
POET recently received additional two 25 million dollar investments
Together with Mitsubishi they develop 3.2Tb transceivers (400Gb/lane)
Far ahead of the market !!
POET is massively increasing its mass production in Asia (China and Malaysia)
With POET’s Lightsources they are at the forefront in chip2chip communication.
The company has NO debt, but around 83Mill $ in Cash
]]>I reckon such spinal flexibility as exhibited in Google’s Apollo networking could be as beneficial there as in a rodeo show (prevents injury), and a great way to save a whole bunch of silver dollars on them there optical AI interconnects too ( https://www.nextplatform.com/2024/08/23/this-ai-network-has-no-spine-and-thats-a-good-thing/ ). A win-win for all involved!
]]>On the “technical” side, Wade expresses Profitability (P) in original (yet valuable) Units of (Token/Second) / (($/token) x Watts), which simplify to (Token/$)x(Token/Joule). The second term scales linearly to units of tokens-per-kilowatt-hour: P_kwh = P x 3.6e⁶ — this keeps the 6x profitability advantage on his plot at 50 tokens/s. If the price of electricity in $-per-kilowatt-hour is C (eg. C = 0.17 $/kWh), then, his Profitability (P) can be converted to P_$, in units of Tokens-per-$, using: P_$ = √(P_kwh/C) = 1897 x √(P/C). With this then, optics have a 2.4x token-per-$ advantage over copper at 50 tokens/s of interactivity — which is still quite valuable IMHO (caveats: all errors mine, and not an economist … capital and operating costs may need separating).
]]>That said, I love the model and the insightful analysis; it helps us think about next-generation systems. It would be valuable to talk to multi-agent startups understand the models they use, the inference speed challenges,and measure the best token rates today.
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