Run Python projects on remote GPUs through MTCode GPU Server and MTCode RemoteGPU β sandboxed, end-to-end encrypted, and usable from VS Code or MTCode Studio without SSH or public IP setup.
GPU access is often the bottleneck in courses, research projects, and experiments. Campus and research-group clusters are frequently oversubscribed: seats are limited, queues can stretch for hours, and hardware refresh cycles often lag behind demand.
At the same time, students, researchers, colleagues, friends, or neighboring labs may already have high-end gaming PCs, lab workstations, or small GPU servers powerful enough for many machine-learning workloads. The problem is not always raw compute β it is safe, controlled access to that compute.
Traditional SSH access gives users broad access to the whole computer, which is not appropriate for machines containing private files, credentials, browser data, unpublished research, or unrelated projects. It is also awkward for temporary GPU sharing between labs, collaborators, classmates, or research groups.
MTCode GPU Server is designed for this gap: controlled Python execution on a GPU-equipped computer without giving users shell access. It can support personal GPU sharing, lab-to-lab collaboration, short-term borrowing, or limited-time rental-style access while keeping execution restricted to a managed sandbox.
MTCode GPU Server runs user Python workloads inside a restricted sandbox with controlled file access, resource limits, and blocked system-level operations.
MTCode RemoteGPU lets users upload projects, start online runs, stream logs in real time, submit offline jobs, and download outputs β all from VS Code or MTCode Studio.
The GPU server remains private. MTCode DirectLink helps with authentication, authorization, and connection setup, while job data flows directly between the client and GPU server over an end-to-end encrypted peer-to-peer channel.