Free AI Hosting
Host AI models for free. GPU access, API hosting, and cloud deployment.
ម៉ាស៊ីនមេពពក
ប្រើ Free.ai ហេដ្ឋារចនាសម្ព័ន្ធ. Zero ដំឡើង, Zero ការថែទាំ. ម៉ូដែលទាំងអស់ត្រូវបានផ្ទុកជាមុនហើយត្រៀមខ្លួនជាស្រេចដើម្បីប្រើតាមរយៈ API ឬ UI បណ្ដាញ.
មានឥឡូវនេះDocker ម៉ាស៊ីនដោយខ្លួនឯង
ដំណើរការម៉ូដែល AI ប្រភពបើកចំហរបស់យើងនៅលើផ្នែករឹងផ្ទាល់ខ្លួនរបស់អ្នក ។ រូបភាព Docker ជាមួយការគាំទ្រ GPU ធ្វើឱ្យប្រសើរសម្រាប់ការសន្និដ្ឋាន ។
សេវាផ្ទាល់ខ្លួនឯកជនដែលបានគ្រប់គ្រង
ម៉ាស៊ីនបម្រើ GPU ដែលបានគ្រប់គ្រងដោយយើងត្រូវបានបញ្ជូនទៅតំបន់ cloud ដែលអ្នកចូលចិត្ត។ ការបំបែកទិន្នន័យពេញលេញនិង SLA ផ្ទាល់ខ្លួន។
ក្រុមហ៊ុនផលិតការចែកចាយដែលបានរៀបចំដោយខ្លួនឯង
ម៉ូដែលរបស់យើងទាំងអស់គឺជាប្រភពបើកចំហ (Apache 2.0 / MIT) ។ អ្នកអាចរត់ពួកវានៅលើហេដ្ឋារចនាសម្ព័ន្ធ GPU របស់អ្នកផ្ទាល់ ៖
# 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 awqតម្រូវការអប្បបរមា
- NVIDIA GPU ជាមួយ 24GB + VRAM (RTX 4090, A5000, A100)
- CUDA 12.0+ និង Docker ជាមួយឧបករណ៍ឧបករណ៍ផ្ទុក NVIDIA
- ប្រព័ន្ធ RAM 16GB +, 100GB + ការផ្ទុកក្នុងមួយម៉ូដែល
- សម្រាប់ម៉ូដែលប៉ារ៉ាម៉ែត្រ 72B: 80GB VRAM (A100) ឬការរៀបចំ GPU ច្រើន
ហេតុអ្វីបានជា Self-Host?
- ភាពឯកជនទិន្នន័យ — Your data never leaves your servers
- គ្មានដែនកំណត់អត្រា — Unlimited inference on your hardware
- ភាពស្របច្បាប់ — Meet data residency requirements
- ការប្ដូរតាមបំណង — Fine-tune models on your data
- ត្រួតពិនិត្យតម្លៃ — Fixed hardware costs, no per-token fees
- ខ្វះខ្យល់ — Runs fully offline
សំណួរដែលសួរញឹកញាប់
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.