Large language models (LLMs) are getting better at handling long conversations and documents, but speed has become a bottleneck. Serving these lengthy requests with low latency and high throughput is a tough challenge, especially as AI gets integrated into more interactive apps and real-time services. A popular technique called speculative decoding (SD) has been used to improve speed, but it was thought to only work well with small batches of requests. MagicDec challenges this assumption by showing how SD can, surprisingly, speed things up even with high throughput and many simultaneous requests, particularly when dealing with longer text sequences. The key insight is that the bottleneck for LLMs shifts as batch size and sequence length grow. MagicDec leverages this to deploy speculative decoding more effectively and improves throughput without compromising accuracy. MagicDec tackles this by understanding how these bottlenecks change and uses “draft models” with efficient memory use to address what slows down large batches and long sequences. The results are impressive: up to a 2x speedup for a LLaMA-2-7B-32K model and a 1.84x speedup for LLaMA-3.1-8B model when handling many requests on multiple GPUs. This breakthrough is significant because it allows LLMs to become faster and more efficient at processing long conversations and documents. This opens up exciting possibilities for many real-world applications, including more responsive chatbots, advanced document analysis, and complex agent workflows. As LLMs continue to grow and handle increasingly complex tasks, MagicDec could help these powerful models become more accessible and useful in everyday applications.