Free GPU Access | Free.ai
Access NVIDIA A100 GPUs for free. Run AI models without buying hardware.
Free GPU-Powered AI Inference
All Free.ai tools run on dedicated NVIDIA GPUs. No GPU required on your end — we handle all inference server-side so you get fast results from any device.
Our GPU Infrastructure
| Spec | Details |
|---|---|
| GPU | NVIDIA A100 / H100 Tensor Core GPUs |
| VRAM | 80GB HBM per GPU |
| Precision | FP16 / BF16 / INT8 quantization |
| Framework | vLLM, PyTorch, ONNX Runtime |
| Hosting | Vultr Cloud GPU (api.free.ai) |
| Network | 25 Gbps+ dedicated bandwidth |
Models Running on Our GPUs
Language Models
- Qwen2.5-72B (Apache 2.0)
- Qwen2.5-Coder-32B (Apache 2.0)
- Mistral-7B (Apache 2.0)
- Phi-3 (MIT)
Image Models
- FLUX.1-schnell (Apache 2.0)
- Stable Diffusion XL (OpenRAIL++)
- Kandinsky 2.2 (Apache 2.0)
Video Models
- CogVideoX-2B (Apache 2.0)
- AnimateDiff (Apache 2.0)
Speech & Audio
- Kokoro TTS (Apache 2.0)
- Piper TTS (MIT)
- MeloTTS (MIT)
- faster-whisper STT (MIT)
- AudioLDM 2 Music (Apache 2.0)
Other Models
- MadLAD-400 3B Translation (Apache 2.0)
- Real-ESRGAN Upscaling (BSD)
- BRIA RMBG 2.0 (Apache 2.0)
- Tesseract OCR (Apache 2.0)
Programmatic Access
Access all GPU models via our REST API. Generate an API key and start making requests in seconds.
curl -X POST https://api.free.ai/v1/chat/completions \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model": "qwen2.5-72b", "messages": [{"role": "user", "content": "Hello"}]}'FAQ
Free.ai runs on NVIDIA A100 and H100 Tensor Core GPUs with 80GB HBM per GPU. These are datacenter-grade GPUs optimized for AI inference with support for FP16, BF16, and INT8 quantization.
No. All AI inference runs on our GPU servers. You can use any device with a web browser or make API calls from any machine. We handle all the GPU processing server-side.
We use vLLM for language model inference, PyTorch for various model types, and ONNX Runtime for optimized inference. These frameworks are chosen for their speed, efficiency, and compatibility with our open-source models.
Our primary GPU inference server runs on Vultr Cloud GPU infrastructure at api.free.ai. Enterprise customers can request deployment in specific regions including US, EU, and Asia-Pacific.
Response times vary by model size. Smaller models like Qwen 2.5 7B respond in 1-3 seconds. Larger models like Qwen2.5-72B take 3-10 seconds depending on output length. Image generation takes 5-15 seconds per image.
Yes. Our GPU infrastructure keeps all self-hosted models loaded and ready. There are no cold starts or loading delays when switching between models.
We use AWQ and GPTQ quantization to fit larger models into GPU memory while maintaining quality. For example, the 72B parameter Qwen model uses AWQ quantization to run efficiently on A100 GPUs.
Yes. All GPU-powered models are accessible via the Free.ai REST API. Generate an API key at the developer page and start making requests using OpenAI-compatible endpoints.
Our GPU servers have 25 Gbps+ dedicated bandwidth to ensure fast data transfer between the inference server and users worldwide.
Yes. Every self-hosted model on Free.ai is open-source. We provide Docker images so you can run them on your own NVIDIA GPU hardware. Minimum requirement is 24GB VRAM for smaller models and 80GB for the 72B models.
Our GPU infrastructure auto-scales to handle traffic spikes. Paid and enterprise users receive priority processing. Free-tier users may experience slightly longer queue times during extreme peak periods.
Self-hosted models run directly on our GPUs with no intermediary, resulting in lower latency and no per-token markup. OpenRouter models access external providers like OpenAI and Anthropic, offering more model variety but with slightly higher latency and a 15% markup on costs.