SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding

SPEED-Bench addresses a specific gap in the LLM optimization space: the lack of standardized evaluation for Speculative Decoding (SD). While SD is a core technique used by frontier labs (OpenAI, Groq) and inference engines (vLLM, TensorRT-LLM), the performance is highly sensitive to the 'draft' model's acceptance rate across different prompt distributions. The project's defensibility is currently low (3) because it acts primarily as a reference implementation for a research paper. Its 9 forks despite 0 stars suggest it is being tracked by academic researchers rather than a broad developer community. The 'moat' for a benchmark is purely social/consensus-driven; if it becomes the standard metric cited in SD papers, its score will rise. However, it faces displacement risk from established inference frameworks like vLLM or NVIDIA's TensorRT-LLM, which could integrate their own 'official' benchmarking suites, rendering third-party tools redundant. Frontier labs are unlikely to build public benchmarks (choosing to keep internal optimizations proprietary), but the rapid evolution of SD techniques (e.g., Medusa, EAGLE, Lookahead) means the benchmark may need frequent updates to remain relevant.