Review Time
I found out the hard way, but I am not the only one. This company seems not to be operational anymore, you start up a server and are never able to connect to it. They take your money, but do not provide the service. Their support does not respond either.
I am very disappointed with this service. Honestly, I tried to setup my VM, with even different instances. Sadly it won't work. And I make one mistake, I realize. I added my credit into it, just to know that I can't use the service anyways. 10 bucks, and just fly like that.Please, do think twice before consider using their services.
The TensorDock GPU deployments does not...contain available GPU! I was trying with 3 different pods that should contain RTX 5090, 4090 and 3090 but...none of them contained the GPU which was clearly stated in the logs of Ollama and also via "lsmod | grep nvidia". Unbeliveable but true - Tensordock is cheating on the customers getting some money to the balance which cannot be refunded, the support does not respond and you cannot run anything with GPU.
I had a disappointing experience with TensorDock's GPU instance deployment. Although the instance showed as deployed and running, I was unable to access it via SSH. Despite being unable to use the service, I was still charged for the time it was supposedly running. As a result, I received zero value from the service while still incurring costs. Until they improve their reliability and access, I would caution against using TensorDock.
Tried to test the service with minimal payment — couldn’t deploy anythingI signed up for TensorDock and made a small initial payment just to test the service. I tried to deploy a GPU instance (RTX 4090), but after going through a setup process (selecting OS, CPU, RAM, SSH key, and location), I got this error:“Failed to deploy virtual machine: No available nodes found in the selected location with the requested resources.”So I tried again with other GPU types — same issue.The worst part is there's no live indication of available GPUs, and the system lets you go through the setup only to fail at the end. For a platform that charges upfront, this is very frustrating. I expected a smoother onboarding and at least one working configuration. Right now, I can't recommend TensorDock to anyone until they improve the deployment experience and show real-time availability.
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