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Speculative Decoding2 articles

Speculative Decoding

Articles

  • Liquid AI Ships LFM2.5-DSpark Draft Models for Up to 3.2x Faster Inference

    Liquid AI has released speculative decoding draft checkpoints for three models across its LFM2.5 series: LFM2.5-1.2B-Instruct, LFM2.5-2.6B, and the mixture-of-experts model LFM2.5-8B-A1B. The release introduces small companion models designed to accelerate auto-regressive generation without altering final token distributions. The draft models are available in Safetensors and GGUF formats on Hugging Face, with immediate support implemented for SGLang and llama.cpp. Architecture and Draft Desig

    1 min

  • Tree-Structured Speculative Decoding: How Multi-Candidate Trees and Tree Attention Accelerate LLM Serving

    Tree-Structured Speculative Decoding: How Multi-Candidate Trees and Tree Attention Accelerate LLM Serving Large language model inference is fundamentally constrained by memory bandwidth during the auto-regressive decoding phase. Because each token generation step requires loading billions of model parameters from high-bandwidth memory (HBM) to compute units for a single token, standard auto-regressive generation operates at low arithmetic intensity. Speculative decoding addresses this bottlene

    1 min