Built forGPU sharing

Carve one NVIDIA GPU into memory-isolated slices for multiple containers — no Kubernetes, no driver patches. Run the installer on any Ubuntu host with an NVIDIA driver and the backend, frontend, and HAMi-core libvgpu image are wired up for you.

$curl -fsSL http://gpu-lambda-blog.vercel.app/install.sh | bash

Prefer to do it yourself? Manual installation instructions.

Isolated shards

Split one GPU into fixed memory slices so each container gets a hard, enforced limit — not a best-effort share.

No Kubernetes

One installer wires up Docker, the NVIDIA toolkit, and the panel. No cluster, no operators, no driver patches.

Full CUDA

Workloads run against the real driver with stock CUDA images — the memory cap is transparent to the code inside.

Secure by default

Each container is confined to its own slice, so one tenant can't reach another's memory or saturate the card.