Chinese #AI #startup #DeepSeek upgraded its V4 model with #DSpark, a #speculativedecoding framework that increases response #speed|s by up to 85%. DSpark uses a lightweight draft model and a larger model to verify responses, reducing the need for powerful chips. https://www.scmp.com/tech/big-tech/article/3358647/faster-ai-lower-costs-dspark-eases-inference-bottlenecks-and-chip-strain-says-deepseek?eicker.news #tech #media #news

Eagle 3.1 resuelve el attention sink en decodificación especulativa y logra 4.79x de speedup en LLaMA-3.3-70B. ¿Vale la migración desde Eagle 3?

Gemma 4’s MTP drafters make speculative decoding a practical local AI decision, explaining how shared KV caches, accepted-token rates, batching, and workload testing determine whether faster generation delivers real-world gains.

Batch size, paralelismo, quantización MXFP4/NVFP4 y kernels: qué mueve el tradeoff y qué expande toda la frontera de serving de LLM.

A deep dive into PagedAttention, speculative decoding, FlashAttention, and continuous batching — the clever tricks that make modern LLMs respond in milliseconds instead of minutes.

A serving engineer watches tokens arrive in that familiar trickle: fast enough to demo, slow enough to feel like the model is still pecking at a keyboard. DFlash matters because it proposes a way out of that rhythm. Here is the real claim in one sentence: DFlash is the first credible path to turning speculative decoding from an optimization trick into a serving architecture, because it removes the hidden assumption that the drafter has to be sequential.

In this series, we aim to provide a solid foundation of vLLM core concepts to help you understand how it works and why it’s emerging as a defacto choice for LLM deployment.