TAHO Labs

Compute scarcity is the most expensive myth in AI. About half the compute you pay for never does any work. Run your numbers and see what you’re leaving on the table. No sign-up needed.

Compute scarcity is the most expensive myth in AI. Most of what you pay for never does any work. Run your numbers and discover what you could save.

Mid-Flight Scaling Demo: Last time we killed a machine mid-job. This time we add one. Three nodes running one job, a fourth sitting offline. Prompt goes in, starts immediately. Then we bring the fourth machine up while the answer is still streaming. Three becomes four on screen, and the new node works on a job it didn't start. Nothing rescheduled. Nothing restarted. Elastic scaling normally means the next job in line gets the new capacity. This one got it mid-sentence. Skeptical? Tell me what you'd need to see in the logs. LMK in the comments.

Mid-Flight Failover: This is the one where we kill a machine on purpose. All devices working the same prompt. Mid-generation, we take one out. The tokens keep coming word by word, and the logs show the survivors register the drop and keep going. Nothing rerouted. Nothing restarted from the top. The usual version of this story is a coordinator noticing a dead worker, requeuing the job, and you pay for the whole thing twice. No coordinator here to notice. Let's go!

Universal Execution Demo: Two machines that have no business being in the same cluster. Top: Linux, two NVIDIA GPUs. Bottom: a Mac Studio M4 Max. One prompt goes in, and both start working. No scheduler, no queue. The logs show them building the response together. Different silicon, different operating system, one fabric across both. Much of the compute a company already owns is not in the GPU cluster. It sits in machines nobody ever wrote a scheduler for. Where can you use this? What challenges come to mind for you?

Compute elimination demo: process once on the fabric, then never re-compute the work you already paid for. Do it without a cache server in front of your machines. Three machines in the fabric, gossiping constantly about what each of them knows. We send the prompt over curl so nothing hides behind a UI. Cold start, nothing cached. The work decomposes, and the answer streams in word by word. Same prompt again. Same endpoint, same machines. It comes back complete immediately. Call it a cache if you want. The difference is where it lives. No cache server in front of this, no node told to hold the answer. The mesh already knows. So push on it. Multi-tenant isolation. Invalidation when the right answer changes. How is caching best controlled in your world? More demos inbound.