Get more from the GPUs you already have.
Training Day develops software to improve the performance and efficiency of AI systems, from GPU workloads to the infrastructure and agents that run them.
Can better software make existing GPUs do more useful work?
Training Day is exploring which software changes could help inference workloads—and what engineers need to know before adopting them.
Why it matters
Adding hardware is not the only way to create headroom.
For teams serving AI models, inefficient software can turn scarce GPU capacity into higher costs or less room to grow. Improvements in the code around inference may help, but teams need confidence that a change preserves the behavior they rely on.
More headroom from software
Training Day is developing software to help teams get more from the GPUs they already have.
The benefit matters only if an improvement holds up on the workload a team actually runs.
Where it stands
Training Day is in private development.
What we are building
Software for GPU workloads, AI infrastructure and agent workflows.
What we are learning
Where better software could increase useful GPU capacity, reduce serving cost, and meet the standards engineers need before adopting a change.
Current stage
Training Day is in private development.
Do you run inference workloads where GPU capacity or cost matters? Talk with Training Day.