Caching Strategies for LLMs

Reduce LLM costs by 60-90% with intelligent caching. Learn semantic, exact-match, and hybrid caching strategies for production deployments.

Semantic Cache

Vector similarity for similar queries

Exact Match

Perfect for repeated queries

TTL Management

Automatic cache invalidation

Semantic Caching Implementation

class SemanticCache:
    def __init__(self, embedding_model="text-embedding-3-small"):
        self.embeddings = {}
        self.cache = {}
        self.threshold = 0.95
        
    async def get_or_compute(self, prompt, llm_func):
        # Generate embedding
        embedding = await self.embed(prompt)
        
        # Find similar cached responses
        for cached_embedding, response in self.cache.items():
            similarity = cosine_similarity(embedding, cached_embedding)
            if similarity > self.threshold:
                return response
                
        # Compute and cache new response
        response = await llm_func(prompt)
        self.cache[embedding] = response
        return response

References

[1] arXiv. "Efficient LLM Inference" (2024)
[2] Hugging Face. "GPU Inference Optimization" (2024)
[3] NVIDIA. "LLM Inference Optimization" (2024)