Every serious inference deployment eventually runs into the same line item: GPU capacity. Whether a team pays per token or per machine, the cost follows how much hardware it takes to serve the traffic. InfraOptimal exists to change the other side of that equation: how much useful work each machine does.
Our company-wide value proposition is short. Autonomous performance optimization across the inference stack: more tokens from the same hardware, while preserving the original model’s quality. Dedicated inference endpoints are the self-serve way to use it today. This note explains what that means and where its limits are.
The inference bill is a hardware bill.
When an AI workload grows, the usual responses are to buy more capacity or to pay a provider more for theirs. Both treat throughput as fixed and scale the hardware around it. But the number of tokens a machine produces is not a property of the machine alone. It depends on how the model is executed: the serving engine’s batching and cache policy, how work is placed across devices, which kernels run, and what the compiler emits for the target processor.
General-purpose serving stacks are built to work well for everyone. That is valuable, and it also means they are tuned for no one’s workload in particular. Request sizes, concurrency and reusable context differ from team to team, and each difference is an opportunity the default configuration leaves on the table.
The speed lives across the whole stack.
We optimize the path from the original model to the instructions running on the device: model execution, the serving engine, device placement, kernel design, and compiler and assembly, with the instruction set and microarchitecture as the hardware target. These layers affect one another. A cache policy changes which kernels matter; a kernel change shifts where the next bottleneck is. Tuning one layer in isolation tends to stop at the boundary of the next.
Fixed reference
Your model and workload
The original model defines the computation. Your traffic and the target hardware define the problem.
Search
Agents propose and test
Changes across serving, execution, placement and kernels run on the real hardware against a baseline.
Result
Only what passes is kept
A candidate that regresses required behavior or performance is rejected; the rest becomes the next baseline.
Agents do the iteration; a fixed contract decides.
Performance engineering has always been iterative: profile, form a hypothesis, change something, measure, repeat. The expensive part is the iteration, and that is the part we give to agents. They propose changes, profile bottlenecks and run experiments within the scope the runtime allows.
What agents do not do is grade their own work. A candidate and its baseline run under the same conditions, and repeated comparisons on held-out workloads decide whether the change qualifies. The objective and constraints are fixed before the search starts. The measured result and its configuration are retained, so the next iteration builds on evidence rather than on a claim.
Quality is the constraint, not the trade-off.
Many ways to make inference cheaper quietly change the model: a smaller variant, a lossy shortcut, a different checkpoint. Our approach starts from the opposite rule. The original model is the reference, and a change that regresses required behavior is rejected, however fast it is.
We state the scope of that rule plainly. Acceptance checks are bounded by the specified workload, the output checks and the hardware conditions they ran under; they are not a proof about every possible input or future machine. And not every optimization run finds an improvement. When nothing qualifies, nothing changes, and your endpoint keeps serving the configuration it already had.
What this means for your team.
Optimization is not a button in the console. The optimized endpoints you can launch today are configurations we prepared and measured before offering them, and each card shows the hardware and the throughput it was measured at. Tuning a running endpoint for your own traffic is a separate engagement: it starts only after an intake with our team and your consent to measure that endpoint, and your model, URL and key stay the same throughout.
The same process applies beyond the self-serve product. If your team runs a large or unusual inference workload, we are glad to look at it with you. Start with how dedicated endpoints are priced, or read what we tell security reviewers.
The hardware your team already pays for has more in it. Our job is to find it, test it against your model, and keep only what holds up.