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

Hugging Face
Read the full article
Researchers have introduced SPEED-Bench, a comprehensive benchmark designed to evaluate speculative decoding methods across diverse language models and tasks. This unified framework aims to standardize performance assessment in speculative decoding research.

More in LLM & Text Generation

PyTorch Conference 2026: Google and Meta Open-Source TorchTPU for Native PyTorch on TPUs

InfoQ (AI, ML & Data)

Nanbeige Releases Nanbeige4.3-33B, a 33B Open Agentic Coding Model

MarkTechPost

Researchers from Stanford and UC Berkeley Introduce TokenGate: Token-Gated Attention Makes Long-Context Prefill Up to 11.3x Faster

MarkTechPost