Compute
The hardware underneath
Every eval and every training run has to land on real silicon. The approach is simple: own the machines that pay off sitting on a desk, and rent datacenter GPUs by the hour when a run needs more than a desk can give. Both halves are Blackwell.
On the desk · unified memory
In the cloud · rented on demand
When a training run or a full sweep outgrows the owned hardware, we burst to datacenter Blackwell — spun up for the job, torn down after.
Why unified memory changes the math
On a normal GPU, model weights have to fit in a few tens of gigabytes of dedicated VRAM, and anything bigger gets copied back and forth across a PCIe straw. Grace-Blackwell stitches the CPU and GPU together over a coherent link into one large memory pool the GPU addresses directly. A quantized 30-billion-parameter model lands in the memory footprint of a mid-size game — and it runs on tensor cores built to do 4-bit math natively rather than emulating it. That’s the lever the whole program pulls on.
This page grows as the fleet does. As we bring platforms onto the bench and collect real measurements, the specs and results land here — and feed straight into the Evals matrix.