B3
@b3labs
AI you own, from bare metal to agentic solutions
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GPU rental prices went up 81 times in 60 days, per FastGPU's report.
The biggest increases were on the chips used for frontier training and production inference:
→ B200: 7 increases (~17.7% each)
→ H200: 6 increases (~17.5% each)
→ H100: 8 increases (~14.3% each)
→ B300: 6 increases (~10.4% each)
For a team renting 8 B200s around the clock, a single $1.20/GPU-hour increase adds roughly $7,000 to the monthly bill.
And when supply is short, the provider controls the price, and you absorb the increase.
It doesn’t have to work that way.
With B3IQ, you own the machine and pay for it once.
We build, host, and run it for you. And you can also rent out the hours you don’t need.
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.@a16z raised $1.1B this week for the hardware AI runs on, from chips and memory to networking and storage.
Hardware makers can grow supply about 20% to 30% a year, and AI demand needs several times that.
A rack that once drew 5 to 10 kilowatts now draws as much as 250, and a16z expects that to reach a megawatt within three years.
The sites running this work increasingly pull power from private sources on top of the grid, and the largest are measured in hundreds of megawatts, with some campuses reaching a gigawatt.
Ben Horowitz, Martin Casado, Raghu Raghuram, David Ulevitch and David George make the case for why every layer of the stack needs rearchitecting.
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Introducing B3IQ.
A new model to own, run and monetize compute.
Already being used by faculty, researchers, and student teams at Stanford, NYU, Penn, UChicago, Dartmouth, Waterloo, and the University of Hawaiʻi.
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