This article outlines how Baseten optimized speculative decoding using the TensorRT-LLM framework to accelerate model inference. The authors detail overcoming technical hurdles such as inefficient batching, hardware contention, and server instability to make the technique viable for production environments. By synchronizing the execution of draft and target models and patching core software bugs, they achieved significantly lower latency, particularly for code generation tasks. The post also highlights the inclusion of essential enterprise features like streaming support, structured outputs, and OpenAI specification compatibility. Benchmark results demonstrate that these refinements can nearly double inference speeds while maintaining high output quality. Source: May 16 2025How we built production-ready speculative decoding with TensorRT-LLMBasetenPankaj Gupta, Justin Yi, Philip Kielyhttps://www.baseten.co/blog/how-we-built-production-ready-speculative-decoding-with-tensorrt-llm/