Qumulator – simulate 1k-qubit circuits on CPU, exact results, no GPU
1000-qubit quantum simulation on CPU using tensor network routing, not statevector.

Live auction across 10 clouds beats manual dashboard hopping for GPU jobs.
ML engineers and data scientists running GPU workloads
SkyPilot · Paperspace Gradient · Lambda Labs
My cofounder and I shared the same frustration: deploying to the cloud shouldn't be harder than running your code locally. Yet somehow it always is.
We also think most cloud usage is wasteful. You shouldn't blindly push everything to the cloud. So we built Verlex, a hybrid system that only deploys to the cloud when your local machine can't handle it. When it does, it picks the cheapest available provider with the exact hardware you need. The entire pipeline is abstracted down to one line of code. You deploy in seconds instead of hours.
We'd love to hear: what frustrates you most about cloud deployment?
1000-qubit quantum simulation on CPU using tensor network routing, not statevector.
Custom streaming filesystem beats Docker pull times for instant GPU job startup.
Per-job GPU cost breakdown where cloud bills and nvidia-smi fail to deliver.
Claude talks to RunPod/Lambda/Lambda/Vast — but needs working provider integrations to matter.
Buzzword-heavy README promises enterprise-grade features but only has 1 GitHub star.
Finally ties GPU metrics to actual workloads when DCGM only gives you uuids.