Running LLMs in production is fundamentally different from demos. The real challenges are infrastructure-level: unpredictable latency, GPU underutilization due to poor batching, cost explosion at scale, and slow autoscaling that reacts after demand spikes. Key optimizations include complexity-based request routing, response
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Demos are easy. Production is a frontier most teams aren't ready to scale.The gap: Why LLMs fail in production but look perfect in demosWhat scaling LLMs really meansThe LLM infrastructure stack behind the modelWhat actually breaks when you scale LLMs in productionPractical LLM optimizations that actually work at scaleThe most common LLM scaling mistakes teams makeThe future of LLM infrastructure beyond the modelFinal thought: LLMs do not fail in isolationMore from We Love Open SourceAbout the AuthorSort: