OrchestraOS

We make AI run faster, on less hardware, on time — and on the chip you choose.

Between your AI model and the chip it runs on sits a layer that decides how much of your hardware you actually use. Today it wastes most of it — and quietly ties you to one supplier.

We replace that layer. Take all of it, or only the part you need.

Talk to us See the proof

Four things get better. Take the ones you need.

Faster

More work out of the chips you already own.

Less hardware

The same work on fewer machines, drawing less power.

On time, every time

Steady response times, for systems where a late answer is a failed answer.

Your choice of chip

Your software should not decide who you buy hardware from.

An operating system for AI execution.

You have models. You have hardware. Something in between decides what runs where, what stays in memory, and in what order the work gets done.

Today that job is split across separate tools that were never built to talk to each other. Throughput is lost there. Money is lost there.

We build that layer properly: compiler, memory and scheduling designed against each other rather than bolted together.

It is modular, not one big block. If all you want is steadier response times, you take that piece and leave the rest.

And it is not only about chatbots. Anything that is a neural network is something we can compile — vision, speech, trading, robotics. Large language models are simply the loudest case right now.

Frozen

When being late is the same as being wrong.

A robot arm or a trading system does not care that the average answer took 20 milliseconds. It cares that one answer in a thousand took 250 — that is the one that breaks something.

That spread is what we remove. It comes from work the machine does behind your back — finding memory, moving pages, compiling while it runs. Frozen settles all of it before the system starts.

Fluid

When you need many AI jobs on one machine.

Agent workloads run out of memory long before they run out of compute — many long-lived jobs, each holding a large context.

Fluid moves a stalled job's state out immediately and brings it back before it is needed, instead of throwing things overboard once the machine is already full.

Two founders, and a fleet of AI agents that writes the code.

The compiler, the runtime and the measurement harness are built by an automated engineering fleet running around the clock under human review. That is how two people ship infrastructure this size.

Friedrich Siemers

Co-Founder

Designs the system — compiler, runtime, and the agent fleet that builds them.

Sebastian Reckzeh

Co-Founder

Owns the commercial side — who buys this, what for, and at what price.

What we have proven.

All of it re-runnable, and we will hand you the artifacts. Where we lose, we say so. What we have not proven comes after this, kept separate on purpose.

1,000 measured runs each, same machine, same model, same input, one request at a time, on a CPU. Typical answer: 14.2 µs for us, 43.6 µs for PyTorch — about three times faster, with identical output, reproduced on a second machine.

The shape matters more than the gap. 979 of our 1,000 runs land in the same half-microsecond; the slowest 1% reach 18.6 µs. PyTorch spreads across 25 µs and settles into two separate peaks — the same work taking reliably different amounts of time. That spread is what we remove, and it is why we build the way we do. These are the raw numbers from the run, not a smoothed curve. We do not claim this result for GPUs.

We can take your model off someone else's runtime	Hand us an open-weight model — weights and a config file — and our compiler rebuilds it, compiles it, and produces output identical to the original, with the framework it came from provably gone from the running system. Done for two unrelated model families; the second took one changed function.
Nothing moves that should not move	On an H200, hardware traces show zero transfers between host and chip across 52,000 kernel launches.
Response times that barely vary	Our slowest answers land within 1% of our typical one. Tuned PyTorch spreads about 12% — measured against a baseline given its best configuration, fixed before the run.
Three times faster on CPU	The comparison in the chart above: identical output, reproduced on a second machine.
It handles real models, not toys	An 8-billion-parameter model ingested with all 739 of its tensors bound into one reproducible package. Running it at that size is the next step.
Where we are still behind	End-to-end on GPU. We match PyTorch's best on raw chip time and lose on the way in and out; the gap is host bookkeeping we are removing. One head-to-head on a consumer GPU we lost outright. We publish those the same way we publish wins.

We have no customers and no revenue yet. Given what we are asking people to believe about infrastructure, better said here than found out later.

Where this goes.

Two things we intend, and have not finished. They are ambitions, not results.

How far the choice of chip actually goes.

Because we own the path from model to chip, the same model can target different silicon without being rewritten. Today that stands on NVIDIA and AMD — real, and narrower than the ambition. Widening it to the rest is work ahead of us.

Eventually, the chips themselves.

Every workload we run teaches us something about how a network maps onto silicon. Collect enough of that and the arrow starts pointing both ways: shaping models to fit the chip, and shaping chips to fit the models.

That is the position we are building toward. We have the machinery that collects it; we have not yet learned anything from it. Saying otherwise would be a story, not a status.

A drawing, not a measurement — we have no numbers here yet, and the axes carry no scale for that reason. What it says: today, once sessions hold more state than fits in GPU memory, the runtime starts evicting under pressure, work gets redone, and throughput collapses rather than levelling off. We are building for the flat line instead — state moved out before the ceiling is reached, and work admitted only when it actually fits. Whether we get there is the open question, and the numbers that would answer it are being measured now.