Cursor has open-sourced Mixture-of-Kittens， the fused mixture-of-experts kernel that lifted its production training throughput by 41%.

A really smart technique here.

Training a big model wastes huge amounts of GPU time just moving tokens between chips， and Cursor got 41% more work out of the exact same hardware by rewriting how that movement is scheduled.

The layer it replaces had become the largest single cost in that training， swallowing more than half of total step time on some workloads.

Earlier fixes attacked only the arithmetic， since custom MXFP8 kernels sped up the expert math and left token shuffling to a separate library.

That division worked while compute dominated， but once the experts got fast enough the transfers became the thing everything else waited on.

The switch to GB300 NVL72 racks helped and hurt at the same time.

All 72 chips now sit on one high-speed link， close enough that a chip can receive its next batch of tokens while still crunching the last one.

But the small CPUs bolted onto those chips are slow， and the GPUs kept finishing early and sitting idle while the CPU decided what to send next.

So the new kernel does the whole round trip， sending tokens out， running the experts， and gathering results back， in one go without ever pausing to check with the CPU.

The biggest gain came from flipping the direction of the traffic， since letting each chip pull the tokens it needs beats having others push them， worth up to 29% more usable bandwidth.

Pulling also kills the "I'm done" messages a push requires， which were costing 5.8x more waiting time in their own tests.

Against the fastest public baseline that adds up to 2.37x faster forward passes， and across 512 production GPUs it lifted throughput from 760.9 tokens per second to 1，070.2.

That 41% came from scheduling choices rather than new hardware， which is the part rival labs should find uncomfortable.

Gains built that way compound across every step of every run， so a kernel rewrite can now beat another rack of chips.