"Efficiently Serving LLM Reasoning Programs with Certaindex"

A system that lets LLMs decide how much compute they need based on problem difficulty

Dynamic scheduling that saves GPU time by measuring when LLMs are ready to answer.

Dynasor , proposed in this paper, is a system that dynamically allocates compute resources for LLM reasoning tasks by tracking model certainty, reducing compute costs while maintaining accuracy.

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https://arxiv.org/abs/2412.20993

🤔 Original Problem:

LLM reasoning programs need multiple solution paths for complex tasks, leading to high compute costs and latency. Current systems waste resources by allocating fixed compute to all queries regardless of difficulty.

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🔧 Solution in this Paper:

→ Dynasor introduces certaindex, a metric measuring LLM's certainty in its reasoning progress

→ It tracks how confident the model is about its current answer path using entropy across different solutions

→ When certaindex is high, it means the model is certain and needs less compute

→ For harder queries with low certaindex, Dynasor allocates more compute to explore alternative paths

→ The system uses gang scheduling to group related requests together, reducing latency

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💡 Key Insights:

→ LLMs can self-assess their confidence while reasoning, enabling dynamic resource allocation

→ Different queries need varying compute based on difficulty - uniform allocation wastes resources

→ Tracking certainty across solution paths helps identify when to stop computation early

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📊 Results:

→ Reduces compute usage by 50% while maintaining same accuracy in batch processing

→ Handles 3.3x higher query rates in online serving

→ Achieves 4.7x tighter latency targets compared to existing systems

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