Cutting inference cold starts by 40x with LP, FUSE, C/R, and CUDA-checkpoint

Modal has achieved a 40x reduction in GPU inference cold starts by implementing four key techniques: maintaining cloud buffers of idle GPUs, using a custom lazy-loading filesystem for container images, and employing checkpoint/restore technology for both CPU and CUDA initialization. These innovations enable serverless GPU scaling from minutes or hours down to tens of seconds, addressing the critical challenge of maximizing GPU allocation utilization for highly variable inference workloads.