Free AI Hosting
Host AI models for free. GPU access, API hosting, and cloud deployment.
क्लाउड होस्ट गरिएको
Free.ai पूर्वाधार प्रयोग गर्नुहोस्। शून्य सेटअप, शून्य रखरखाव। सबै मोडेलहरू पहिले लोड गरिएका छन् र एपीआई वा वेब यूआई मार्फत प्रयोग गर्न तयार छन्।
अहिले उपलब्धडोकर स्व-होस्ट गरिएको
तपाईँको आफ्नै हार्डवेयरमा हाम्रो खुला स्रोत AI मोडेलहरू चलाउनुहोस्। GPU समर्थनको साथ डोकर छविहरू, inference को लागि अनुकूलित।
स्व-सेवाप्रबन्धित निजी
समर्पित GPU सर्भरहरू हामी द्वारा प्रबन्धित, तपाईंको प्राथमिकता क्लाउड क्षेत्रमा तैनात। पूर्ण डाटा अलगाव र अनुकूलित एसएलए।
उद्यमस्व-होस्ट गरिएको वितरण
हाम्रो सबै नमूनाहरू खुला स्रोत (Apache 2.0 / MIT) हुन् । तपाईँले तिनीहरूलाई तपाईँको आफ्नै GPU पूर्वाधारमा चलाउन सक्नुहुन्छ:
# Pull and run a model with Docker
docker pull ghcr.io/free-ai/inference:latest
docker run --gpus all -p 8000:8000 ghcr.io/free-ai/inference:latest \
--model qwen2.5-72b --quantization awqन्यूनतम आवश्यकता
- २४ जीबी+ भिआरएम (RTX ४०९०, A५०००, A१००) सँग एनभीडिया जीपीयू
- CUDA 12.0+ र NVIDIA कन्टेनर टूलकिटको साथ डोकर
- 16GB + प्रणाली RAM, प्रति मोडेल 100GB + भण्डारण
- 72B परिमिति मोडेलहरूको लागि: 80GB VRAM (A100) वा बहु-GPU सेटअप
किन Self-होस्ट?
- डेटा गोपनीयता — Your data never leaves your servers
- दर सीमा छैन — Unlimited inference on your hardware
- अनुरूपता — Meet data residency requirements
- अनुकूलन — Fine-tune models on your data
- लागत नियन्त्रण — Fixed hardware costs, no per-token fees
- एयर-गाप — Runs fully offline
FAQ
Three options: Cloud Hosted (use our infrastructure, zero setup), Docker Self-Hosted (run models on your own GPU hardware), and Managed Private (dedicated GPU servers managed by us in your preferred region).
You need an NVIDIA GPU with 24GB+ VRAM (RTX 4090, A5000, A100), CUDA 12.0+, Docker with NVIDIA Container Toolkit, 16GB+ system RAM, and 100GB+ storage per model. For 72B parameter models, you need 80GB VRAM or a multi-GPU setup.
Yes. Self-hosted deployments run fully offline once the Docker images and model weights are downloaded. This is ideal for air-gapped environments and sensitive data processing.
Pull our Docker image and run it with GPU support. The command is: docker run --gpus all -p 8000:8000 ghcr.io/free-ai/inference:latest --model qwen2.5-72b --quantization awq. The container handles model loading and serves an API endpoint.
All self-hosted models use permissive open-source licenses -- Apache 2.0, MIT, or BSD. You can use them commercially without restrictions. We deliberately exclude models with restrictive licenses like Meta's Llama license.
Managed private hosting gives you dedicated GPU servers in your preferred cloud region, fully managed by our team. We handle setup, patching, model updates, and monitoring. You get full data isolation with an enterprise SLA.
Yes. Since all models are open-source, you can fine-tune them on your own data using standard training frameworks like Hugging Face Transformers. Our Docker images are compatible with popular fine-tuning tools.
Contact our sales team to discuss a trial period. We typically offer a short evaluation period for enterprise prospects to test managed private hosting before committing to a long-term plan.
Cloud hosting uses the standard token-based pricing. Self-hosted is free -- you only pay for your own hardware and electricity. Managed private hosting is priced based on GPU allocation, region, and SLA level.
Yes. You can self-host specific models for high-volume or sensitive workloads while using the Free.ai cloud for everything else. The API format is identical, making it easy to route requests between your infrastructure and ours.
We provide documentation, Docker images, and community support for self-hosted deployments. Managed private hosting includes full technical support, monitoring, and a dedicated account manager.
Cloud hosted is best for teams that want zero maintenance. Self-hosted is ideal for data privacy, compliance, or unlimited usage on your own hardware. Managed private is the best of both worlds -- full data isolation with no operational burden.