Less Compute

Run the same AI workload on less compute.

The same quality at a lower cost per result. We compress the model you run, tune it to your hardware, and prove it against your current setup on your own task.

Start your workload brief

Built by machine-learning researchers with more than 15,000 citations and a former Google product lead.

Your hardwarefixed capacityYour workloadwaiting in the queue

How it works

Four steps on one workload, measured against the setup you run today.

  1. Step 1: Compress the model.

    We make your model smaller. Smaller alone isn’t cheaper: what counts is the cost of each result.

    One of your racksOriginal sizeSmallerafter compression

  2. Step 2: Validate it on your task.

    Compression can change behavior, so we check the smaller model against your own examples and retrain it until it passes.

    Examples from your workloadFails first, then passesretrained until it does

  3. Step 3: Tune it for your hardware.

    We adapt how the model runs to the machines you already have, so the same hardware does more work. That is what lowers the cost of each result.

    The goal: more workmore work on the same rackTuned to your hardwareThe smaller modelrunning on your rack

  4. Step 4: Benchmark it against today’s setup.

    Your current setup and ours run the same tests on your task. The goal: the same quality at a lower cost per result, proven before you commit.

    • Your current setup
    • Less Compute
    Cost per resultThe goallower cost per result
    Your current setupCost per resultLess ComputeThe goal: lower cost

Start with one workload.

Tell us what your model does and where cost or capacity is holding it back. We’ll come back with a plan to run it for less.

Start hereone rack, tunedThe goal: a row to sparesame work, a row to spareYour workload

Your workloadTell us about it.

Four short answers and where to reply. Sent via FormSubmit to hello@lesscompute.ai.

Common questions

  • Will quality hold?

    That is what step 2 is for. The smaller model is retrained until it meets the requirements we agree with you, and you see the benchmark before you commit.

  • We use a closed model API. Is this relevant?

    Not today. We work on models your team runs and can adapt.

  • What if our setup is already optimized?

    Then that setup is the baseline we have to beat.

Start your workload brief