Hussam Ali @Electricaliee

@EthanCastr @ArtificialAnlys Interesting, so this would kind of be like pricing models closer to how much they would cost in GPU hours I guess. Great stuff

When are there going to be price/duration pricing for LLMs? Mercury 3 is running at 1000 tok/s $0.70 per million output 16.667 min to one million output tokens (uninterrupted token streaming) Vs Haiku 5.5 runs at 232 tok/s (according to @ArtificialAnlys) $0.50 per million output tokens ~72 min to one million output tokens So you’re [let’s call it] investing your $ into tokens, 5x faster (omitting price). Of course, this isn’t a big deal because you would ideally get to your outcome faster and still spend the same amount of tokens (if not less because Mercury 3 is reportedly smarter than Haiku 5.5).

Actual “decision tree”. @Raspberry_Pi 5 running @liquidai d1 600m Being powered by solar With a diy tree connected to Figuring out how to productize this for < $125

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@EthanCastr @Raspberry_Pi @liquidai My goat

@IanBallantyne @MaartenGr Awesome stuff!!

@EthanCastr @reflection_ai @NVIDIAAI @togethercompute Had a great time!! Thank you to the hosts!

Had a good time going to the Open by Design event hosted by @reflection_ai @NVIDIAAI @togethercompute with @Electricaliee If you’re interested in local, edge, open source ai, let’s chat!

Try out @liquidai new LFM 2.5 24B model routed by their D1 decision model, made by us @offlinebase. Website in comments. @EthanCastr

@oldmangrypto @jason_of_cs I can confirm, as discussed I got that shock Fall of 2022! And each time a huge breakthrough happens it’s just shock after shock but to lesser degrees each time

@willcb Just signed up, will be there!!

@IanBallantyne Exciting stuff, I also can’t wait to see what others build!!

@danylo_dev Well said. Might as well struggle anyway though!

@adamzvada Awesome stuff!! I’ll see you at Greptile X Datadog happy hour in 30 min!

@AamishBeg @danylo_dev @ycombinator Congrats!!!!

@satyanadella So exciting

Today we are announcing our biggest bet yet on our own silicon. Starting next calendar year, we will scale our Maia family to multi-gigawatt deployments, making it the primary inference system for our own product and services. And we’re introducing Maia 300, which delivers 40% more tokens per watt compared to leading GPU-based systems. We are already deploying Maia 200 across the US, Europe, and Australia, delivering up to 1.7x more tokens per watt across experiences in Teams and Copilot. Our goal is to build on the full-stack efficiency gains we’ve already seen, while continuing to bring the latest innovations from across the ecosystem into our fleet.

@EthanCastr @fcvprzhfgsybj Ai through fpga so cool!!

@EthanCastr @litcapital This sounds great thank you I’ll look into them

@EthanCastr @khoomeik This is awesome thank you!

@EthanCastr @francesca_lab Oh yeah? Watch me.