This research introduces a novel approach that significantly speeds up LLM inference by combining multi-level speculative decoding with quantized draft models.

• Achieves up to 3.3x speedup over baseline inference
• Maintains accuracy comparable to full 16-bit precision models
• Uses variable-sized draft models in a multi-level cascade approach
• Implements efficient quantization (INT4/INT8) to reduce memory footprint

For engineering teams, this breakthrough means faster, more efficient LLM deployment without sacrificing quality, potentially reducing inference costs and enabling more responsive AI applications in production environments.

ML-SpecQD: Multi-Level Speculative Decoding with Quantized Drafts