immers.cloud is a cloud GPU platform that lets you host virtual servers equipped with powerful NVIDIA GPUs: ideal for everything from cloud gaming and 3D rendering to machine learning and running large language and generative AI models.
We offer pay-as-you-go-billing, meaning you pay only for the exact time your cloud server is running.
The sign-up process takes seconds: no long forms, no approvals, no waiting. Create your first GPU instance and start working within minutes of registration.
Our cloud GPU servers are used for:
When you host a GPU server, you get full, dedicated access to the hardware. Everything is managed through an intuitive web dashboard or via OpenStack API.
immers.cloud offers the widest range of cloud graphics cards in Russia —13 NVIDIA RTX and Tesla models, including high-performance GPUs like the NVIDIA H100 and A100 with 80 GB of memory, support for GPUDirect and NVLink technologies, as well as the latest H200. This enables faster computation and handling of larger datasets.
We were the first company in Russia to implement virtualization technology for these GPUs, making server hosting in our data center even more flexible and accessible.
Our technical support team is available 24/7 to assist with any technical issues.
To accelerate your workflow, we provide virtual machines with preinstalled software: Ubuntu, Windows Server, CUDA, Docker, vLLM, and more. You can deploy a cloud GPU server with your preferred OS and software stack in just a few clicks.
Immers Foundation Models is a platform for inference and working with large language models. The product is aimed at developers and product teams who need to run large language models on servers with a predictable budget, without dependence on external APIs and token-based payments.
The catalog eliminates engineering and economic barriers in the industrial deployment of LLMs. Instead of independently calculating video memory requirements, selecting weights, quantization, and configuring the inference backend, the user receives ready-made, tested configurations with a transparent assessment of cost and performance. This allows for quickly launching projects or optimizing company workflows using AI without capital expenditures on hardware.
Each model undergoes manual engineering validation. Original weights and tested quantizations (4, 8, and 16 bits) are available in the catalog, which allows for selecting a balance between generation quality, hardware availability, and hosting budget. For each weight variant, the following are calculated: context size, VRAM consumption, available memory for user requests, and maximum parallelism. Additionally, generation speed and throughput (tokens/sec) benchmarks are provided. The system automatically selects compatible GPU configurations — from single GPUs to clusters with multiple nodes and software load balancers.
Models are deployed on hosted GPU servers in Russia. Payment is charged exclusively for the actual operating time of the virtual machine or dedicated server. The user retains full control over the inference stack: chooses the vLLM version, configures access, and scales resources as needed. To simplify deployment, recommended deployment parameters and 24/7 technical support are available.
In addition, Immers Foundation Models offers ready-to-use Public Endpoints with the most popular and effective open-source AI models deployed on the immers.cloud infrastructure. This is an excellent opportunity to access the capabilities of large language models without hosting a GPU server. Payment for working with Public Endpoints is charged for the tokens computed and generated by the model. At the same time, there are also free models in the list — an ideal option for learning to work with LLMs and prototyping projects.
A defining feature of immers.cloud is the use of immersion cooling—a server cooling technology where hardware is fully submerged in a dielectric fluid, delivering:
Thanks to immersion cooling, immers.cloud ensures consistent high performance—critical for long-running, resource-intensive tasks such as neural network training and inference.