Running large language models (LLMs) locally requires powerful GPUs. Whether you want to run Llama 4, Mistral Large, or DeepSeek R1, you need access to GPUs with sufficient VRAM. Fortunately, there are many cloud providers offering on-demand GPU servers at reasonable prices — from $0.10/hr for an RTX 3090 to $2+/hr for the latest H100.
For beginners, we recommend RunPod or Vast.ai. RunPod offers a simple interface, Docker containers, and serverless GPU. Vast.ai is a marketplace where you'll find the lowest prices thanks to its peer-to-peer model. Both support popular frameworks like vLLM, text-generation-inference, or Ollama.
For production deployment of AI agents, Lambda Cloud or CoreWeave are suitable. Lambda offers dedicated A100 and H100 instances optimized for deep learning. CoreWeave targets enterprise customers with guaranteed availability and SLA. Both providers have direct integration with PyTorch and other ML frameworks.
If you're looking for the best price-to-performance ratio, consider Tensordock or Salad Cloud. Tensordock offers GPU servers from $0.10/hr, Salad Cloud uses a distributed network and achieves even lower prices. For demanding enterprise applications, there are dedicated servers from Latitude.sh with full bare metal access.
Choosing the right GPU is key. For models up to 13B parameters, an RTX 4090 (24GB VRAM) is sufficient. For 70B models, you need an A100 (80GB) or multiple GPUs. For training custom models, H100 or the new GB200 are ideal. Find a complete overview of providers on our GPU Servers for AI page.