The cost of a token is the limit.
Every useful thing built on a language model is rationed by what a token costs and how long it takes to arrive. We exist to push both down, every week, for as long as the endpoint exists.
Why we started
Open-weight models closed most of the quality gap. The serving stack did not. The same model on the same GPU can differ by two or three times in tokens per second depending on precision recipe, kernels, batching policy and decode strategy, and the stock configuration is almost never the right one for a given workload.
Most teams tune once, at launch, and never again. Traffic shifts, models update, new silicon lands, and the endpoint quietly falls behind what the hardware could do. The people who know how to fix it are scarce and expensive, so the fix waits for the next quarter.
What we believe
Optimization should be continuous, not a project. The right unit of work is the endpoint, and the right cadence is always. That is only possible if agents do the searching, and people set the goals.
Correctness comes before speed. A configuration that is faster and slightly wrong is a liability. Every change we ship clears a correctness gate against a full-precision reference and the customer’s own evals first.
Hardware is an empirical question. Nobody should buy accelerators on a spec sheet. We measure the workload on every accelerator we can reach and report tokens per dollar, including when the answer is not the hardware you were about to buy.
Every endpoint should learn from every other. A kernel found for one architecture, a bit-width that held for one model, a batching recipe that worked for one traffic shape: each becomes part of the search space for all of them.
Publish the numbers. Methodology, configs and failure cases. If a result would not survive a skeptical engineer’s scrutiny, it does not go on the site.
The mission
Make every token cheaper and faster every week by letting agents, not release cycles, optimize the serving stack. Cheap, fast, correct inference is the substrate for everything else people want to build with these models, and the ceiling on it is engineering, not physics.
The company
Deep Inference is a small, early-stage team of systems engineers and the agents they built. We run today in our customers’ clouds, on their GPU quota; dedicated capacity on partner clouds is coming. We do not put faces on the website. We put numbers, and we invite you to check them.
Reach us at hello@deepinference.co or on X.
Come measure it with us.
Bring a workload, leave with numbers. Tell us what you are serving and we run it on the silicon that fits.