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New AI cascade of LLMs - FrugalGPT (Stanford)
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ChatGPT and GPT-4 can be expensive for a small enterprise, like $21K per month to support customer service. How to reduce the price (costs) for ChatGPT and GPT-4 services with PROMPT adaptation, LLM approximation (caching solutions and fine-tuning Transformer sub-systems) and/or LLM cascades?
Data-adaptive LLM selection!
Stanford University published new insights how to save costs and increase AI system performance, for you as a business owner or a AI developer. The optimal way to use LLM systems like ChatGPT or GPT-4 (also valid for GPT-5) to reduce the price you have to pay (to OpenAI, Microsoft, ...).
Thevideo highlights the challenges faced by small businesses in the United States that use GPT-4 for customer service, as the monthly costs can be around $21,000. Additionally, the energy and environmental impacts of running LLMs in cloud compute centers are significant. To address these issues, the authors compare the costs associated with 12 different commercial LLMs and introduce the concept of frugal GPT.
Frugal GPT aims to reduce the resources needed to run LLMs by focusing on prompt adaptation, LLM approximation, and LLM cascading. Prompt adaptation involves decreasing the length of the input prompt, while LLM approximation utilizes an external cache to store previous query responses. By checking the cache before submitting a new query, the system can avoid unnecessary interactions with the LLM. LLM cascading involves using different LLMs based on cost and performance, starting with cheaper options like GPT-3 and progressing to more expensive models like GPT-4 if necessary.
The authors conducted experiments using different LLM APIs and prompting strategies and found that frugal GPT can achieve substantial efficiency gains. In one specific case using a news dataset, they reduced the inference cost of running queries by 98% while still exceeding the performance of GPT-4. The implementation also involved a scoring function, which could be a regression model trained on a dataset of headlines using a simplified version of BERT.
The video emphasizes the potential cost savings and improved performance achieved by using a multi-AI system with various price points and performance levels. By utilizing frugal GPT and considering different LLM options based on cost and quality, significant cost reductions and even accuracy improvements can be achieved compared to using a single expensive LLM like GPT-4.
The YT video also briefly mentions the idea of going even further in reducing costs by using even lower-quality LLMs and providing additional semantic linguistic information to compensate for their limitations. However, this hypothetical scenario would require longer prompts and potentially contradict the prompt adaptation strategy discussed earlier.
Overall, the publication presents an approach to reduce the cost of using LLMs in cloud computing environments while maintaining or improving performance. The frugal GPT concept and the use of multiple LLMs with varying costs and performance levels offer potential benefits for businesses seeking cost-effective solutions for their AI applications.
All rights (data, model, charts and tables) are with the authors of the scientific preprint:
FrugalGPT: How to Use Large Language Models
While Reducing Cost and Improving Performance
by Lingjiao Chen, Matei Zaharia, James Zou
Article by Markettechpost:
#performance
#price
#ai
#gpt4
#chatgpt
#explained
#insights
Data-adaptive LLM selection!
Stanford University published new insights how to save costs and increase AI system performance, for you as a business owner or a AI developer. The optimal way to use LLM systems like ChatGPT or GPT-4 (also valid for GPT-5) to reduce the price you have to pay (to OpenAI, Microsoft, ...).
Thevideo highlights the challenges faced by small businesses in the United States that use GPT-4 for customer service, as the monthly costs can be around $21,000. Additionally, the energy and environmental impacts of running LLMs in cloud compute centers are significant. To address these issues, the authors compare the costs associated with 12 different commercial LLMs and introduce the concept of frugal GPT.
Frugal GPT aims to reduce the resources needed to run LLMs by focusing on prompt adaptation, LLM approximation, and LLM cascading. Prompt adaptation involves decreasing the length of the input prompt, while LLM approximation utilizes an external cache to store previous query responses. By checking the cache before submitting a new query, the system can avoid unnecessary interactions with the LLM. LLM cascading involves using different LLMs based on cost and performance, starting with cheaper options like GPT-3 and progressing to more expensive models like GPT-4 if necessary.
The authors conducted experiments using different LLM APIs and prompting strategies and found that frugal GPT can achieve substantial efficiency gains. In one specific case using a news dataset, they reduced the inference cost of running queries by 98% while still exceeding the performance of GPT-4. The implementation also involved a scoring function, which could be a regression model trained on a dataset of headlines using a simplified version of BERT.
The video emphasizes the potential cost savings and improved performance achieved by using a multi-AI system with various price points and performance levels. By utilizing frugal GPT and considering different LLM options based on cost and quality, significant cost reductions and even accuracy improvements can be achieved compared to using a single expensive LLM like GPT-4.
The YT video also briefly mentions the idea of going even further in reducing costs by using even lower-quality LLMs and providing additional semantic linguistic information to compensate for their limitations. However, this hypothetical scenario would require longer prompts and potentially contradict the prompt adaptation strategy discussed earlier.
Overall, the publication presents an approach to reduce the cost of using LLMs in cloud computing environments while maintaining or improving performance. The frugal GPT concept and the use of multiple LLMs with varying costs and performance levels offer potential benefits for businesses seeking cost-effective solutions for their AI applications.
All rights (data, model, charts and tables) are with the authors of the scientific preprint:
FrugalGPT: How to Use Large Language Models
While Reducing Cost and Improving Performance
by Lingjiao Chen, Matei Zaharia, James Zou
Article by Markettechpost:
#performance
#price
#ai
#gpt4
#chatgpt
#explained
#insights
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