Your next GPU is the one
you already own.

More room to build. Less energy to waste. We’re working on a better
return from the hardware—and power—that drive your AI.

An imagined compute instrument with its ivory cover removed, revealing a green circuit board, black chips, gold traces, and silver cooling fins.

01 / GPU workload tuning

More headroom for real workloads on the hardware you already own.

02 / CUDA kernel work

Focused work on latency, throughput, and memory efficiency.

03 / Verifiable progress

Repeated correctness and regression checks, with evidence your engineers can inspect.

04 / Agent evaluation

Judge agent-driven changes by correctness, useful results, and the resources they consume.

05 / Power efficiency

The goal: more useful work per watt, so your available power goes further.

THE POSSIBILITY

More useful work.
Same hardware.

Your ideas shouldn’t have to wait for another generation of hardware. We’re focused on the software opportunity: getting more useful work from the compute you already have. Finding that headroom takes careful systems work—and evidence that stands up to scrutiny.

Better is something
you can verify.

Improvement has to survive repeated testing. We check correctness, compare results under consistent conditions, and look for regressions as the work evolves.

Correctness · controlled comparisons

More useful work.
Per watt.

Power can be the limit before hardware is. We’re focused on better performance per watt: more useful throughput within your power budget, or less energy to complete the same work.

Power efficiency · useful capacity

Evidence your team
can work with.

Give another engineer more than a headline number. Inspectable results and clear comparison context make it easier to review a change, understand its limits, and decide what deserves the next run.

Inspectable results · informed decisions
TRAINING DAY

Building for the
next constraint.

Training Day focuses on optimizing GPU workloads, CUDA kernels, and agent loops—across the inference stack.

We bring systems optimization and disciplined agent evaluation into the same effort: finding useful changes, testing them carefully, and making the outcome understandable. The value is in the work between an idea and a result you can trust.

Our foundation is local evaluation with results you choose to share. We’re developing it for teams whose next constraint is performance, cost, or confidence in what their agents produce.

Explore the CPU evaluation workbench

FOUNDER AND TEAM

Julie Bush, Founder and engineer

Julie is the sole founder and leads product development and engineering.

Training Day is a product of Sourceflow LLC, registered in California.

LET’S MAKE MORE POSSIBLE

What would you do
with more headroom?

Tell us what you’re running—and whether GPU capacity, power,
or speed is holding you back.

Start a conversation