OpenAI’s AI models like DALL-E 2, ChatGPT, and GPT-3 have become incredibly popular in recent years. However, accessing these models requires an OpenAI API key which provides a limited amount of free credits. Once the free credits are used up, you need to purchase additional credits which can get expensive.
Fortunately, there is a way to self host OpenAI models and get unlimited credits for free – by running them on your own machine. In this comprehensive guide, we will go over everything you need to know to get started with self hosting OpenAI in 2023.
Overview of OpenAI Account Credits
Before we dive into self hosting, let’s first go over some background on how OpenAI credits work.
OpenAI provides developers with access to its AI models through API keys. Each API key comes with a set amount of free credits which are consumed each time you make an API call to generate text, images, etc.
For example, here are the free credits provided with OpenAI’s pricing plans:
- OpenAI API – $18 of free credits
- OpenAI API Plus – $100 of free credits
- OpenAI API Pro – $300 of free credits
The amount of credits consumed depends on the model, prompt length, and parameters used. Generating content with GPT-3 Davinci which is OpenAI’s most capable AI text model costs about $0.02 per 1,000 tokens.
Once your free credits are used up, you have to purchase additional credits to keep accessing the API. The costs can add up quickly, especially for apps with high usage.
This limit on free credits exists so OpenAI can monetize access to its AI models. But for individuals and small companies, the credit costs can be prohibitive.
Benefits of Self Hosting OpenAI
Self hosting OpenAI models lets you bypass the free credit limits and generate unlimited AI content for free. Here are some of the biggest benefits:
No Usage Limits
With self hosted OpenAI, you don’t have to worry about staying within usage quotas. You can generate as much AI content as you need without incurring any API costs.
Your self hosted models will perform faster since requests don’t have to go over the internet to OpenAI’s servers. Local inference reduces latency allowing for more real-time applications.
Sensitive data never leaves your machine preventing any privacy issues. This is especially important for personal or business applications dealing with confidential information.
You can fine tune or optimize self hosted models for your specific use case instead of relying on OpenAI’s general purpose training. Focused custom models can be more accurate for niche applications.
If OpenAI’s cloud servers ever go down or throttle usage, your self hosted models will keep working unaffected by any outages. This provides redundancy and business continuity benefits.
Requirements for Self Hosting OpenAI
Before you can start self hosting OpenAI models, your local machine needs to meet some minimum requirements:
- GPU: You absolutely need an Nvidia GPU with at least 12GB of VRAM for decent performance. RTX 3090, A5000 or better is recommended.
- CPU: A modern multi-core CPU (AMD Ryzen or Intel Core i9)
- RAM: 64GB or higher
- Storage: 2TB SSD
- Nvidia GPU Drivers: Up to date GPU drivers (at least 470.x+ version recommended)
- Docker: Used for running the model containers. Docker CE or Docker Engine 20+
- Nvidia Container Toolkit: Enables GPU acceleration in Docker
- Python 3.8+: For scripts and integration code
Alternatively, you can use cloud services like AWS EC2 to host deployment which removes local hardware restrictions.
OpenAI Models for Self Hosting
Now let’s go over the various OpenAI models available for self hosting unlimited credits:
GPT-3 is OpenAI’s groundbreaking natural language model capable of generating human-like text for a wide variety of applications. The full GPT-3 model is too large for self hosting, but there are smaller versions that work well:
- Ada: A smaller 357M parameter version of GPT-3. Ada can generate coherent multi-sentence text like blog posts, articles, and summaries.
- Babbage: Babbage has 13B parameters making it very capable for long form content while being self hostable on high end hardware.
- Curie: Curie is a 6.7B parameter general purpose GPT-3 model for advanced text generation.
Codex models are trained by OpenAI to generate and understand code. This makes them useful for programming applications:
- Coder: The 2.7B parameter Codex model for generating code snippets and summaries based on natural language prompts.
- Code Parrot: A lightweight 1.6B parameter version of Codex focused on generating code from text.
DALL-E models can generate realistic images and art from textual descriptions:
- DALL-E 2: The full 12B parameter DALL-E 2 model is too large for self hosting. Butsmaller versions are available.
- DALL-E 1: The original DALL-E model with 1.5B parameters can run locally to generate simple art and imagery.
Whisper is OpenAI’s speech recognition and translation model. Local versions can transcribe audio to text:
- Whisper.en: An English focused Whisper model for speech-to-text transcription.
- Whisper.fr: French version of Whisper for transcribing French audio.
CLAIRE is OpenAI’s conversational chatbot focused on being helpful, harmless, and honest. Self hosted versions like CLAIRE-90M work well for customer service chatbots.
Downloading and Setting up OpenAI Models
There are two main methods to download and deploy OpenAI models for self hosted credit generation:
Most self hosted OpenAI implementations involve running models in Docker containers. Here are some repositories with prebuilt Docker images:
With Docker containers, you can quickly pull and start generating AI content. But customizing the models requires directly editing the code.
For full customization and control, you can manually install OpenAI models by cloning model repositories like:
The code can be edited directly allowing advanced users to retrain, fine tune, and optimize models for their needs.
Integrating Self Hosted OpenAI Models into Apps
To use self hosted OpenAI models within your own applications, you’ll need to integrate them through code:
Most models provide a Python API allowing easy integration into Python apps and scripts. Generate images, text, code, etc and process it further in Python.
Python APIs can be wrapped into a REST service and consumed by apps written in C#, Java, .NET etc. Python microservices architecture works well.
For quick testing, models provide a CLI to generate text, images, etc right from the terminal. Useful for experimentation before full integration.
Cost Analysis of Self Hosting vs OpenAI Credits
Let’s do a quick cost analysis comparison of running self hosted OpenAI versus paying for OpenAI credits:
Self Hosted OpenAI
- Hardware Cost: $3,000 – $5,000 for GPU server
- Monthly Hosting Cost: $60+ for cloud hosting like AWS
- Total Annual Cost: Approximately $1,000 per year
- 1M tokens with GPT-3 Davinci: $20
- 10M tokens per year: $200
- 100M tokens per year: $2,000
For light usage, OpenAI credits are cheaper. But once you exceed 10M+ tokens, self hosted starts becoming significantly more cost effective.
Plus self hosting provides other benefits like lower latency, higher control, and customization as discussed earlier.
Limitations and Risks of Self Hosted OpenAI
Before jumping into self hosting OpenAI models, be aware of the limitations and potential risks:
- Requires expensive hardware and technical expertise to setup and maintain.
- Self hosted models may not generate content as accurately or coherently as OpenAI’s full size models.
- You are responsible for monitoring and filtering any harmful or biased output generated by the models.
- Models could be misused to spread misinformation or generate spam/phishing content if not properly secured.
- OpenAI may attempt legal action if self hosting violates their usage policies, especially for commercial use. Check OpenAI’s current policy before deploying self hosted models.
Self hosting OpenAI models allows you to unlock unlimited free access to capabilities like text and image generation with GPT-3 and DALL-E. This guide provided a comprehensive overview of the benefits, requirements, setup options, optimization techniques, integration strategies, and costs of operating self hosted OpenAI models in 2023. Make sure to carefully evaluate the limitations and risks before deployment. With the right precautions, self hosted OpenAI can be immensely valuable for personal and business applications requiring large amounts of AI generated content.
What are the benefits of self hosting OpenAI models?
The main benefits are no usage limits, improved performance, enhanced privacy, customization options, and redundancy if OpenAI servers go down. You also avoid needing to purchase additional credits.
What hardware do I need to self host OpenAI?
You need an Nvidia GPU with at least 12GB of VRAM, a modern CPU, at least 64GB of RAM, and 2TB of SSD storage. RTX 3090 or A5000 GPUs are recommended.
How do I set up self hosted OpenAI models?
You can use prebuilt Docker containers from repositories or manually install models by cloning repositories like Anthropic’s Claude or GEN-SDK. Docker is the easiest method.
What OpenAI models can be self hosted?
GPT-3 versions like Ada, Babbage, and Curie work well for text generation. For images, DALL-E 1 can run locally. Codex, Whisper, and CLAIRE models are also options.
How can I customize and optimize self hosted models?
Fine tuning on custom data, prompt engineering, model scaling, parallelization, quantization, knowledge enhancement, and pipelining multiple models together allow customizing self hosted models.
How do I integrate self hosted models into my apps?
Is self hosting OpenAI legal and safe?
You are responsible for monitoring outputs to avoid harmful content. Usage must comply with OpenAI’s policies, especially for commercial use. There are risks of misuse as well. Consult OpenAI’s current policies.
What are the costs compared to OpenAI credits?
Self hosting costs around $1000/year for hardware and hosting. OpenAI credits cost $0.02 per 1000 tokens, so over 10M tokens per year self hosting becomes much cheaper.