How Speculative Decoding Achieves 3x Faster LLM Inference Without Losing Quality: The Mathematics Behind Draft-Verify Acceleration
The sequential nature of autoregressive language models creates a fundamental bottleneck: generating each token requires a full forward pass through billions of parameters. A 70B parameter model processing a single token must load roughly 140GB of weights from memory (FP16), and memory bandwidth—not compute—becomes the limiting factor. This is why a 70B model might generate only 20-30 tokens per second on an H100, despite the GPU being capable of orders of magnitude more computation. ...