NVIDIA CLUSTERS ACTIVE

Free GPU VPS for AI & Image Generation

Accelerate your creativity with Free GPU VPS hosting. Optimized for Stable Diffusion XL, Forge, and DeepSeek-R1. No credit card required. Experience hardware-accelerated AI for 180 days.

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vGPU Partitioning with NVIDIA CUDA® Support

Image generation and deep learning training require parallel processing. Our Free GPU VPS uses NVIDIA vGPU partitioning to deliver dedicated CUDA cores directly to your KVM instance. This ensures your Stable Diffusion renders happen in seconds, not minutes.

  • NVIDIA L4 / A10: Enterprise-grade AI Tensor cores.
  • CUDA 12.4+ Ready: Full support for the latest AI libraries.
  • High VRAM Allocation: Optimized for SDXL and Flux models.

One-Click Stable Diffusion Setup

Deploy the Automatic1111 WebUI instantly on your GPU VPS with this command:

# 1. Update and install dependencies
wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh

# 2. Verify GPU passthrough
nvidia-smi

# 3. Launch with API access
bash webui.sh --share --xformers

GPU AI Benchmarks

Stable Diffusion XL

~2.5s / Image

512x512, 30 Steps, DPM++ 2M Karras

DeepSeek-R1 (32B)

~18 Tokens/Sec

High-speed reasoning via Tensor acceleration

LLM Training

LoRA Ready

Fine-tune your own models with dedicated VRAM

Full Model Context Protocol Support

Build AI agents that can actually *do* things. Our GPU nodes fully support MCP servers, allowing your hosted LLMs to interact with your local databases, file systems, and external webhooks in a secure, isolated environment.

  • Connect LLMs to SQL/Vector Databases
  • Build Autonomous MCP-Ready Agents
  • Secure Local Data Sandboxing

Verified Inference Speeds

Flux.1 [dev]

~12s / Step

High-Fidelity Image Gen

DeepSeek-R1 (Distill 14B)

~22 Tokens/s

Complex Reasoning Tasks

Stable Video Diffusion

~4s / Frame

Generative Video Inference

Personal Fine-Tuning Sandbox

Use our dedicated vGPU memory to train your own LoRA (Low-Rank Adaptation) models. Perfect for training your face into Stable Diffusion or fine-tuning Llama on your specific writing style.

# Run Kohya_ss for LoRA training
bash setup.sh --use-cuda --vram-optimization-level 2

Enterprise vGPU Virtualization

Other "Free" Trials

Shared Docker containers with software-emulated GPUs. Result: Massive latency, websocket drops, and memory crashes.

GratisVPS Infrastructure

True KVM Passthrough with dedicated PCIe lanes. Your bot gets direct hardware access to CUDA cores and VRAM.

Pre-Installed AI Tooling

PyTorch 2.4

TensorRT

CUDA 12.6

NVIDIA-Docker

JupyterLab

GPU AI Hosting Technical FAQ

1. What is the VRAM limit on the Free GPU tier?

Our free trial provides a dedicated 4GB or 8GB vGPU partition (NVIDIA L4/A10), which is optimal for running Stable Diffusion 1.5, XL (Turbo), and quantized DeepSeek-R1 models.

2. Does it support NVIDIA CUDA® and cuDNN?

Yes. Every instance supports CUDA 12.4+ and cuDNN. You can verify your acceleration instantly by running nvidia-smi in the terminal.

3. How do I update NVIDIA drivers on my VPS?

Drivers are pre-installed in our AI templates. However, with root access, you can manually update via apt install nvidia-driver-580 or the latest Data Center branch.

4. Can I run Automatic1111 or ComfyUI?

Absolutely. Our hardware-accelerated nodes are designed specifically for these WebUIs, supporting xformers for massive speed boosts in image generation.

5. Is this shared or dedicated GPU?

We utilize GPU Passthrough (vGPU). While the physical card may be partitioned, your assigned VRAM and CUDA cores are 100% reserved and isolated from other users.

6. How can I monitor my VRAM usage?

You can use the command watch -n 1 nvidia-smi to see real-time memory allocation and temperature while your AI models are processing.

7. Can I use this as a backend for my own AI app?

Yes. Every GPU VPS includes a static public IP. You can expose ports like 7860 (Gradio) or 11434 (Ollama) to create a private AI cloud API.

8. Is it suitable for Model Fine-Tuning?

The free tier is optimized for Inference. For LoRA training or large-scale fine-tuning, we recommend upgrading to our A100 clusters for higher VRAM throughput.

9. Does the GPU go to sleep when idle?

No. We utilize Persistence Mode on our NVIDIA drivers. The GPU remains initialized 24/7, ensuring sub-second response times for every prompt.

10. Is my AI model data secure?

Yes. KVM virtualization provides hardware-level encryption and kernel isolation, ensuring your proprietary weights and datasets are 100% private.