Rent GPU resources for LLM inference, fine-tuning, deep learning and generative AI. Choose from dedicated GPU VPS and bare metal AI servers with full root access, persistent storage and predictable monthly pricing.
Compare AI GPU server plans for LLM inference, AI training, fine-tuning, generative AI, and other GPU-accelerated workloads.
Choose an AI GPU server based on your model size, workload and memory requirements. These recommendations are typical starting points for training, fine-tuning and inference.
Suitable for quantized language models, development, experimentation and lower-concurrency inference.
A balanced option for larger quantized models, longer contexts and moderate production inference.
Designed for larger quantized LLMs, extended context workloads and higher-memory inference.
Suitable for LoRA, QLoRA and other fine-tuning workflows where memory capacity and stability matter.
Recommended for memory-intensive training, larger datasets and workloads that benefit from multiple GPUs.
Suitable for image generation, upscaling, ControlNet, batch workflows and custom ComfyUI pipelines.
Actual GPU memory requirements vary by model architecture, precision, quantization, context length, batch size and framework. Check the selected configuration before ordering.
GPU Mart provides dedicated GPU infrastructure for AI training, LLM inference, fine-tuning and generative AI. Choose an AI GPU server based on your workload, required VRAM and budget.
Run AI workloads with dedicated GPU resources instead of competing for GPU time on a shared or interruptible instance.
Use flat monthly plans for persistent AI workloads and avoid unexpected usage-based billing during long-running projects.
Install and manage your preferred operating system tools, NVIDIA drivers, CUDA stack, containers and AI frameworks.
Keep models, datasets, dependencies and configurations available between sessions without rebuilding the environment.
Choose from GPU VPS, bare metal dedicated servers and multi-GPU configurations as your AI workload grows.
Get assistance with server access, networking, hardware availability and infrastructure-related deployment issues.
Install and manage your preferred AI software with full root or administrator access. GPU Mart AI servers support common frameworks and deployment tools for training, fine-tuning and LLM inference.
Build, train, fine-tune and deploy deep learning models with NVIDIA GPU acceleration.
Run machine learning, computer vision and neural network workloads using GPU-enabled TensorFlow environments.
Develop and test deep learning models through a streamlined, high-level interface built around TensorFlow.
Download, fine-tune and deploy open-source language, vision and multimodal models.
Run and manage open-source LLMs through a simple self-hosted model environment.
Serve language models with continuous batching and optimized GPU memory management.
Build custom containers and manage your preferred CUDA, driver and dependency versions.
Develop, test and document AI workflows in an interactive notebook environment.
Software compatibility depends on the selected operating system, GPU model, NVIDIA driver and CUDA version. Review framework requirements before deployment.
Build your own LLM server or generative AI environment with dedicated GPU resources, persistent storage and full control over models, frameworks and dependencies.
Deploy open-source language and multimodal models using Ollama, vLLM, Hugging Face Transformers or your preferred inference and fine-tuning stack.
Create a persistent GPU environment for image generation, upscaling and custom visual workflows without rebuilding your models and dependencies between sessions.
Something you need to know about AI servers, GPU for AI workloads, AI hosting, and LLM server deployment to help you choose the right solution.
Choose an AI GPU server based on your workload, required VRAM and budget. Compare dedicated GPU options for training, fine-tuning, LLM inference and generative AI.
