Hi everyone, Angel here from the Numerata team. We built Lupine because getting GPU compute usually forces a bad trade. You either reserve capacity and pay for it while it sits idle, or you rent serverless GPU, rewrite your code into someone else's framework, and send your data to their cloud to run it. We wanted the elasticity people go to the cloud for, on the hardware teams already own, with no rewrite and nothing leaving the network. Lupine is a drop-in CUDA shim, so your existing PyTorch, JAX, or TF code runs unmodified. What that unlocks: Spin up GPUs on demand, scale to zero when idle. Compute starts when a job arrives and releases the moment it finishes, so your spend tracks actual work instead of allocated hardware. Run inference at scale without managing servers. No control plane to stand up, no fleet to babysit. Deployment is two containers. Fine-tune models without reserved capacity. Grab GPUs for the run, release them after. No minimum commitment sitting idle between experiments. Burst to 100+ GPUs for distributed training. Pool the machines you already own into one fleet, run the job, then release every GPU when it's done. All of it runs inside your own environment, private cloud or fully air-gapped, so your models and data stay put. That's the whole idea: elastic compute on your own hardware, with your code and your data where they belong. Sign up now to get $20 in GPU credits - console.lupine.sh/login

Lupine

Attach GPU's to CPU-only machines on demand

Attach GPU's to CPU-only machines on demand