Choosing the Right LLM Implementation for Classification Tasks

When implementing LLM-based classification systems, architects must choose between 7 main approaches:

Implementations Overview

Implementation	Description	Resource Usage	Latency	Accuracy	Best For	Not Recommended For
Quantized Fine-Tuned	Specialized model trained for specific task	Low	< 100ms	High	High query volume, Fast responses, Low cost per inference	Frequent updates, Complex edge cases
Frontier Fine-Tuned	Fine-tuned GPT-4/Claude model	Very High	500ms-1s	Very High	Maximum accuracy, Enterprise budget, Complex classifications	Cost constraints, High volume needs
RAG + Frontier Model	Vector DB + Latest LLM	High	1-2s	Very High	Dynamic knowledge base, High accuracy, Complex reasoning	Strict latency, Budget constraints
RAG + Quantized	Vector DB + Compressed LLM	Medium	500ms-1s	Medium	Balance of speed/cost, Regular updates, Medium scale	Very high accuracy, Simple fixed patterns
Instruct Frontier	Direct prompting of latest LLM	High	1-2s	High	Low volume needs, Varied use cases, Quick deployment	High volume, Strict consistency
*Hybrid Fine-tuned	Multiple specialized models with voting	High	200-300ms	Very High	Mixed complexity tasks, High accuracy, Moderate scale	Simple classifications, Budget constraints
*Hybrid Instruct	Multiple frontier models with voting	Very High	2-3s	Very High	Maximum accuracy, Complex varied tasks, Enterprise scale	Cost sensitivity, Speed requirements

Real World Applications

Real-World Example

Consider an O*NET job code classifier:

# Example: Quantized Fine-Tuned Approach
response = model.predict("What is the O*NET code for: Economists")
# Returns: 19-3011.00 in <100ms