How to Reduce Your OpenAI Spend by up to 90% with Small Language Models

OpenAI has revolutionized the way enterprises build with large language models. A developer can create a high-performing AI prototype in just a few days, but when it’s time to push to production, the cost of GPT-4 skyrockets, oftentimes reaching hundreds of thousands of dollars a month. The result: fewer use cases deployed, fewer users engaged, and more value left on the table.

So, what does it take to reduce your OpenAI spend? A new trend is emerging in the open-source. Smaller task-specific models, along with techniques like multi-LoRA serving, can reduce your OpenAI bill by 10x. And best of all, you don't need to share data with a commercial model vendor.

Join our interactive discussion and live demo to learn:
• The true cost of OpenAI and how to model spend for the future
• Three ways to lower your OpenAI bill with open-source:
1. How to fine-tune small LLMs that beat GPT-4 for only $8
2. How to reduce inference costs with multi-LoRA serving
3. How to increase token throughput with speculative decoding
• Real world case studies on how companies reduced spend by 90%
• Live demo

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• Download the webinar slides: https://pbase.ai/4c28BKt
• Get started with $25 in free Predibase credits: https://predibase.com/free-trial

Видео How to Reduce Your OpenAI Spend by up to 90% with Small Language Models канала Predibase