Systems
The GPUs are here, but not enough of them. That's the truth for most mid-sized biotech and life sciences compute clusters, anyway. You’ve got your eight A100s, maybe sixteen if you're lucky, sitting in a rack next to your fast storage and your CPU lanes.
Systems
Mike ("MJ") Joseph stepped onto the stage recently and began unpacking Meta’s bold pivot toward Passive Optical Networking (PON) within its hyperscale datacenters, it seemed unlikely that anyone present would instinctively connect suburban broadband infrastructure to the computationally dense, intense world of life sciences research. But hold
Systems
The funny thing about revolutions (and yes, the transition to AI-driven drug discovery certainly qualifies) is how they unfold gradually, right under our noses. If you had blinked sometime around, say, 2019, you might have missed the moment when major pharma firms stopped treating AI as an intriguing experiment. Instead,
Systems
At the bleeding edge of drug discovery, researchers are facing a new kind of bottleneck. Models balloon to trillions of parameters, datasets surge into exabyte territory, and suddenly the GPU-driven datacenter begins to wobble under the load. Inside pharma labs, research hospitals, and genomics shops, computational scientists increasingly find themselves
Systems
Recursion Pharma's stack is bioIT future spelled out clearly from automated labs through edge GPU compute streaming Kafka Kubernetes orchestration and cloud TPUs The architecture isn't just efficient it's inevitable. It’s almost a cliché now to say biology has become a data science
Power
Why the next frontier in compute might leave your infrastructure behind In the beginning, there was brute force. You fed the GPUs data—any data, all data—and they ground it into insight with raw, burn-the-planet math. Bigger models meant bigger training runs meant bigger clusters. And the datacenter, that