How SlipStream gets more out of every GPU

In our internal benchmarks, SlipStream serves up to 3× the throughput of baseline serving. Throughput is where serving cost comes from: every extra token per GPU-hour is compute you don't have to buy.

Traffic rises and falls through the day, and what your users ask for shifts with it. Acceleration set up for one pattern falls behind when the pattern changes. SlipStream learns from live traffic, adapts when it moves far enough to matter, and holds steady through small swings.

One deployment usually serves several jobs, each asking your model for very different text. Each use case adapts on its own: when support chat traffic shifts, your overnight batch keeps its acceleration.

A response is rarely one kind of text. A single turn can move from reasoning to a tool call to an answer written for a person, and acceleration helps each of those by a different amount. SlipStream adapts to each part as your model writes it.

You decide what each use case should prioritize, and every adaptation aims at it. Here's how the three modes compare on the same simulated day.

Run SlipStream in our cloud, inside your own cloud account, or on GPUs you own. See what you control in each option.

Bring the open-weight and fine-tuned models you already host. You keep the weights.