Hybrid Inference Routing for Coding Assistant COGS Reduction
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Solution Overview
Problem
The high costs associated with using large language models, including cloud hosting fees and network latency, outweigh the benefits due to low acceptance rates of model outputs by users in software development environments.
Innovation Solution
A hybrid inference system combining a local generative pre-trained transformer model with a remote large language model, utilizing a routing model to predict output acceptance, routing tasks to either model based on user acceptance likelihood, thereby reducing cloud hosting costs and network latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large language model is hosted on an external web server, then the model can provide natural language processing and text generation capabilities, but the cost associated with usage (including web service fees and network latency) increases and may outweigh the benefits
Solution Approach 1:
The system segments the language model processing into two parts: a large language model for complex tasks requiring external hosting, and a smaller local model for simpler tasks that can be handled locally. This segmentation allows the system to optimize cost by using the local model for routine operations while maintaining access to advanced capabilities when needed.
Solution Approach 2:
A routing model is introduced as an intermediary component that determines whether to use the local model or the external large language model for each input. This mediator analyzes the input characteristics and predicts which model will provide the better cost-performance balance, thereby reducing overall COGS while maintaining quality.
2Adaptability or versatility
If a large language model is hosted on an external web server, then the model can perform various tasks, but network latency is incurred in communicating with the external web server
Solution Approach 1:
The system divides processing tasks between local and external models based on complexity. Simple, time-sensitive tasks are handled locally by the smaller model, eliminating network latency. Complex tasks that require the full capabilities of the large language model are routed externally, accepting the latency trade-off only when necessary.
Solution Approach 2:
The routing model performs preliminary analysis of each input before it reaches the main processing stage. By predicting the appropriate model in advance, the system avoids unnecessary network communication for tasks that can be handled locally, thereby reducing overall latency.
3Adaptability or versatility
If a large language model is hosted on an external web server, then the model can generate output, but the acceptance rate of the output by users may be low
Solution Approach 1:
The routing model uses feedback from historical data about user preferences and input characteristics to make informed decisions about which model to use. By learning from past performance patterns, the system can predict which model is more likely to generate acceptable output for a given type of input, thereby improving the overall acceptance rate.
Data Source
AI summary
A hybrid inference system for a coding assistant utilizes a routing model to predict whether output generated by a large language model for a given prompt would be accepted by a user of the coding assistant. The routing model routes the prompt when the routing model indicates that the output generated by the large language model is likely to be accepted. The routing model routes the prompt to a local model when the output generated by the large language model is not likely to be accepted. The routing model is trained on the historical output generated by the large language model for various prompts and the acceptance or rejection of the output by users of the coding assistant.