Lupine

Lupine optimizes GPU resource utilization by acting as a transparent CUDA shim, allowing your existing PyTorch, JAX, or TensorFlow code to run on a dynamic fleet of GPUs. Key capabilities include: • On-demand GPU access with instant cold starts • Automatic scaling to zero when idle, eliminating wasted costs • Aggregation of GPUs across multiple machines into a single, unified pool • Drop-in integration requiring no code changes • Secure operation within private clouds or air-gapped environments This platform addresses the common challenge of underutilized GPU infrastructure, enabling organizations to pay only for the GPU time they consume. It transparently proxies CUDA calls with minimal overhead, supporting essential libraries like cuBLAS, cuDNN, and NVML. Whether running on Mac, Linux, ARM, or x86, users can execute commands on remote GPUs as if they were local, making GPU resources elastic and accessible. Lupine is ideal for engineering teams, researchers, and developers who need flexible, cost-effective access to GPU power. It supports a variety of use cases, from spinning up gpus on demand and scaling inference workloads to fine-tuning models without reserved capacity and bursting to hundreds of GPUs for distributed training.