Most LLM inference guides push speculative decoding as the silver bullet for speed. But when I ran the numbers on Llama 3, MoE architectures cut costs by 3.2x for the same throughput—if you're willing to trade off a specific kind of latency.
The conventional wisdom is simple: speculative decoding uses a small draft model to predict tokens, then verifies them with your big model in parallel. Batch verification is faster than sequential generation, so you get 2-3x speedups. MoE (Mixture of Experts) feels like the opposite bet—keep the model huge, but activate only a fraction of parameters per token. Both claim to solve the same problem: making large language models cheaper to run.
But they optimize for completely different bottlenecks. And if you're deploying Llama 3 in production, picking the wrong one can triple your infrastructure spend.
Speculative Decoding: The Latency Tax Nobody Mentions
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