Scaling LLMs in Production: Lessons from the Trenches
A practical guide to deploying large language models at scale, covering inference optimization, caching strategies, and cost management.
By Lisa Zhang
Deploying large language models in production presents unique challenges that go far beyond model selection. In this article, we share hard-won lessons from helping enterprises scale their LLM deployments.
Inference Optimization
The first bottleneck most teams hit is inference latency. Batch inference, quantization, and speculative decoding can each provide 2-5x improvements. Combined, they can reduce latency by an order of magnitude.
Caching Strategies
Semantic caching — where semantically similar queries reuse cached responses — can cut API costs by 40-60% in production workloads. The key is choosing the right embedding model for similarity matching.
Cost Management
Token economics matter. Implementing smart routing between model sizes based on query complexity can reduce costs by 50% while maintaining quality. Start with monitoring, then optimize aggressively.
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