SpecDiff Analytics
Implementation and high-fidelity analysis of the SpecDiff research paper. Real-time benchmarking of speculative diffusion decoding using Masked Diffusion Language Models (MDLM).
The Challenge
LLMs are slow because they generate tokens one-by-one. Autoregressive decoding is limited by memory bandwidth, making inference expensive at scale.
SOTA Methodology
We use Speculative Decoding with a Masked Diffusion (MDLM) draft. This state-of-the-art approach allows for non-autoregressive token proposals.
Hardware Context
Optimized for NVIDIA T4 GPUs. We prove that SOTA speedups are achievable on limited infrastructure through clever algorithm tuning.
Key Impact
We achieve up to 2.5x speedup gains across various models while maintaining exact mathematical parity with standard decoding.
Initialize Analytics
Drag & drop your experiment CSV logs to start exploration