High latency is the primary bottleneck for delivering responsive, user-facing large language model (LLM) applications. How can you significantly accelerate LLM inference without sacrificing model accuracy? Red Hat’s Mark Kurtz and Megan Flynn examine speculative decoding, a technique that uses a smaller, faster model—the "speculator"—to draft multiple tokens ahead of the main model, or the "verifier". The result is lossless inference acceleration, leading to faster, cheaper, and high-accuracy LLM deployments. 🔗Read more about Speculators: https://developers.redhat.com/articles/2025/11/19/speculators-standardized-production-ready-speculative-decoding 00:00 Introduction 00:45 The Latency Challenge in LLMs 03:57 What is Speculative Decoding? 16:04 User Case Flow with Speculators 17:22 Current Capabilities and Roadmap 18:26 Why EAGLE3? (A Leading Decoding Algorithm) 19:20 Pretrained Speculators, Ready to Deploy 19:58 One-Command Deployment Example 20:40 Measuring Speculator Effectiveness 22:38 What to Expect in Performance 24:09 Composing Speculative Decoding with Quantization 27:14 Creating and Adapting Your Own Speculators 29:13 Key Takeaways & Conclusion #RedHat #AI #LLMinference #speculators

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