You have been paying for cold starts
Weights stay mapped and the graph stays compiled between calls. The first request after an idle hour costs what the second one does.
residentIntroducing
The inference runtime that gets out of the way. Models in, tokens out, nothing left running.
Eclipse keeps weights resident and cold-starts in the time it takes to parse the request, so the runtime stops being the thing you plan around.
Weights stay mapped and the graph stays compiled between calls. The first request after an idle hour costs what the second one does.
residentThe same build runs on the laptop you developed it on and the rack you deployed it to. No per-target rebuild, no quiet precision swap.
portableIdle means idle. When traffic stops the runtime releases and the meter stops with it, which is the part every other bill forgets.
idlePoint Eclipse at a model file. It picks the backend for the machine it's on, compiles the graph once and keeps it warm. There is no server process to stand up and no container to keep alive.
quickstart$ pip install eclipse
import eclipse
model = eclipse.load("./weights/7b-instruct")
# backend picked here, once. graph stays warm.
for tok in model.stream("Say hello"):
print(tok, end="")
Invented product, invented figures. Nothing here was measured.
GGUF, safetensors and ONNX, quantised or not. Eclipse reads the file as it is, so there is no conversion step and no exported copy to keep in sync.
Apple silicon, consumer and datacentre GPUs, and plain CPU. The backend is chosen when the model loads, so the same artifact moves between them unchanged.
Per second of active serving. When traffic stops the runtime releases and the meter stops with it, so you never pay for capacity sitting idle.
Yes. The runtime ships as a single binary you can run on your own machines. The hosted service is that same binary running on ours.
On the hosted service, requests are processed in memory and not retained. Self-hosted, nothing leaves your machines at all.
The interesting part of a runtime is what it stops doing. Put a model in front of it and see how little is left between the request and the first token.
You're on the list. We'll be in touch when a seat opens.