GPU Servers for Machine Learning and Self-Managed AI Inference

Yes. You can use the same dedicated GPU server for model training, fine-tuning, testing, evaluation, and self-managed AI inference.

Compatibility and performance depend on the selected GPU configuration, model size, VRAM requirements, framework, precision format, batch size, context length, and multi-GPU support.

You receive root or administrator access and can configure the environment required for your project. Compatibility depends on the operating system, GPU model, driver version, CUDA version, and framework requirements.

Self-managed AI inference means that MaxCloudON provides the dedicated GPU server, operating system, and full administrative access.

You install and manage your own AI models, inference frameworks, libraries, API endpoints, security settings, monitoring tools, and application environment.

A ready-to-use model or managed API endpoint is not included in the standard GPU server plan. If you require a preconfigured or ready-to-use AI deployment, contact us to discuss your model, workload, and technical requirements.

Yes. You can deploy compatible open-weight language models, custom fine-tuned models, and models available through Hugging Face or other model repositories. You are responsible for checking the model licence, downloading the model, installing the required libraries, and configuring the inference environment.

Compatibility depends on the model architecture, model format, required VRAM, precision or quantization method, framework, CUDA version, and selected GPU configuration.

Yes. You can install compatible model-serving software such as vLLM, Text Generation Inference, or another framework that exposes an OpenAI-compatible API.

You are responsible for installing and configuring the inference engine, deploying the model, securing the endpoint, managing API keys, configuring HTTPS, setting access controls, and maintaining the software environment.

MaxCloudON does not provide a ready-to-use OpenAI-compatible endpoint as part of the standard GPU server plan. If you require a preconfigured or ready-to-use AI deployment, contact us to discuss your model, workload, and technical requirements.

The appropriate GPU configuration depends on:

• Model size and parameter count
• Required GPU VRAM
• Training or inference workload
• Precision and quantization format
• Batch size
• Context length
• Number of concurrent requests
• Dataset size
• Framework and multi-GPU support

Send us your model, framework, and expected workload, and we will recommend an available server configuration.

MaxCloudON does not charge per input or output token. You pay a fixed price for the selected daily, weekly, or monthly GPU server plan.

The number of requests or tokens the server can process depends on the GPU configuration, model size, inference framework, context length, batch size, and concurrency.