GPU Servers for Machine Learning and AI Inference

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

Performance depends on factors including model size, required VRAM, framework, precision or quantization format, batch size, context length and multi-GPU support.

MaxCloudON provides the GPU infrastructure, operating system and administrative access.

You install and manage your own models, inference software, libraries, API endpoints, security configuration and application environment.

A ready-to-use managed AI API is not included in the standard service.

Yes. You can deploy compatible open-weight models, custom fine-tuned models and models obtained from Hugging Face or other repositories.

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

Yes. You can install compatible serving software such as vLLM, Text Generation Inference or another framework capable of exposing an OpenAI-compatible API.

You remain responsible for deploying the model, securing the endpoint, managing access and maintaining the software environment.

The appropriate configuration depends on:

• model size and parameter count
• required GPU VRAM
• training or inference workload
• precision and quantization format
• batch size
• context length
• expected concurrency
• dataset size
• framework
• multi-GPU support

Send us your workload requirements and we can recommend an available configuration.

No. MaxCloudON does not charge per input or output token.

You pay the fixed price of the selected daily, weekly or monthly infrastructure plan.

Actual model throughput depends on the GPU configuration, model size, inference framework, context length, batch size and concurrency.