Applied math, supercomputing, trains, banjos, county courthouses, college football, county courthouses, Steely Dan, "HPC," mismatched, black cats
BHM/ Bmore/ A2/ STL
Joined August 2021
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Always start singing this to myself when I remember I'm a Southerner in the Midwest and don't really have any idea what's going on over there in the Bay youtube.com/watch?v=FPJx3G5L…
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leighton retweeted
This is a thoughtful explanation of how @cerebras manages parametric yield at wafer scale. For those interested in the nitty gritty of chip design, i think you will enjoy it.
Extremely excited to see us roll out Kimi K3.1, and proud I got to implement and own one of the backing wafer kernels for this model.
One of the great things about @cerebras is we're all empowered to wear lots of different hats. Very grateful to contribute to the inference stack alongside other Cerebras initiatives
Moonshot just dropped Kimi K3.1 Pro. We used it to speed run online shopping.
One prompt → monitor found → checkout → order confirmed.
11.5 seconds.
Watch @Kimi_Moonshot K3.1 Pro in action, accelerated by Cerebras.
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leighton retweeted
@OracleCloud will be offering @cerebras in OCI for high-speed inference.
The best companies offer the best choices to their customers. So excited to be working with @Oracle.
OCI customers will have another high-speed inference option for building AI agents that can respond quickly and tackle demanding tasks. Cerebras AI Inference on OCI is planned for 2027: social.ora.cl/6016BHoFum
#AIWorld
leighton retweeted
Yesterday, Oracle and Cerebras announced that Oracle Cloud Infrastructure (OCI) will offer Cerebras’ fast inference to AI labs, enterprises, and developers.
After more than a year of extensive evaluation of Cerebras technology, Oracle selected Cerebras to bring the world's fastest AI inference to its cloud infrastructure.
We're excited to bring blazingly fast inference to more AI labs, enterprises, and developers around the world. Together with Oracle.
Trying to channel my inner @MilksandMatcha and learn how to leverage AI to make really cool content so here's an illustration of mapping RF emulation to the @cerebras wafer-scale engine
leighton retweeted
It's so weird that amidst the 21st century tech boom, the Bay Area has actually *lost* pro sports teams since apparently none of these tech geniuses is normal enough to want to be a local hero and own a baseball team.
leighton retweeted
Your AI agent has a to-do list. Waiting shouldn’t be the longest item.
Cerebras’ Andy Hock joins theCUBE to discuss why inference speed matters for reasoning models and AI agents.
Where would less waiting make the biggest difference?
Watch the full conversation with Andy Hock and theCUBE:
youtube.com/watch?v=H2GnroPz…
leighton retweeted
The main thing I notice AIs do that humans don't is write lots of scripts running searches and verifications.
They don't work in a small search space, and they aren't using a gigabrain directly on a large search space. They're just writing lots of scripts to search large spaces that would be annoying to do.
When I watch them work, I mostly think, "Oh I probably should've been doing that. But I'm just so lazy, I would never crank out 1000 lines of Python doing a bunch of Grobner basis or group theory calculations just to test a hypothesis." Even if it would've been more reliable and less work overall.
Your computer runs billions of clock cycles per second, and AIs will actually use it for thinking.
I also think: If math had "industrial value", then this would've been systematized and you could train people to search for math solutions like an AI. Maybe Bel is superhuman, but lots of people could be an Astra if the computational approaches were written down and taught.
It's the difference between "stare at a paper, think, hope a pattern comes to mind" vs. "What is a good search space? Let me feed that into an SMT solver or CAS or custom C++ searcher." Of course, many people do that, but not as fluently and consistently as AI.
What does a superhuman level of mathematics look like? Here is one elementary example I could give to nonexperts.
Daniel Kane and Terence Tao showed (arxiv.org/abs/1702.00912) that, given a collection X of sets, the total number of ways to write A as a disjoint union of B and C, with A, B, C from X (disjoint means non-overlapping), is at most cardinality of X to the power 1.72598…, and this power is sharp.
To show this, they came up with an innocent-looking function B(x,y,z) = (xyz)^c of three variables, satisfying a certain arithmetic inequality and having prescribed boundary conditions. Proving that arithmetic inequality was really a headache.
There was a question whether one could improve this bound, i.e., maybe add some logarithmic correction terms, and one could kind of imagine what should be done (see comments on terrytao.wordpress.com/2017/…), but it was not clear whether it would be easy to do.
Now superintelligence improves this bound by introducing a certain monster function of three variables (see screenshot). Even if I had seen this function in my dreams, I would never have dared to verify that main arithmetic inequality.
The candidate function produced by SI gives a bound matching the known lower-bound examples up to a constant factor, including the sharp logarithmic correction.
All the details are here paatai.github.io/ai-proofs/b…
Our 2023 Gordon Bell Finalist with KAUST on seismic processing sc23.supercomputing.org/2023…
My first time really writing deep-in-the-weeds kernel code for our architecture. I'd like to think this was a stake in the ground on the power of SRAM-heavy arches for scientific workloads.
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DRBE, my main focus for the last two years. I've been deeply fortunate to spend the last couple years drive this capability forward and now see it as a personal passion project
darpa.mil/news/2025/drbe-dev…
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