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Your GPUs are probably idle most of the day, and you're paying for every hour of it. Lupine fixes that. It's a drop-in CUDA shim: your PyTorch, JAX, or TF code runs unmodified on GPUs that scale to zero when idle and pool across every machine you own into one fleet. Private cloud or fully air-gapped. No rewrite, no reserved capacity, no idle bill, and nothing ever leaves your network.
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
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About Lupine on Product Hunt
“Attach GPU's to CPU-only machines on demand”
Lupine was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #111 on the daily leaderboard. Your GPUs are probably idle most of the day, and you're paying for every hour of it. Lupine fixes that. It's a drop-in CUDA shim: your PyTorch, JAX, or TF code runs unmodified on GPUs that scale to zero when idle and pool across every machine you own into one fleet. Private cloud or fully air-gapped. No rewrite, no reserved capacity, no idle bill, and nothing ever leaves your network.
Lupine was featured in Developer Tools (518.5k followers), Artificial Intelligence (477.4k followers) and Tech (631.3k followers) on Product Hunt. Together, these topics include over 367.5k products, making this a competitive space to launch in.
Who hunted Lupine?
Lupine was hunted by Angel Catalan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Lupine stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Angel Catalan