<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Speculative-Decoding on Noureddine RAMDI</title><link>https://ramdi.fr/tags/speculative-decoding/</link><description>Recent content in Speculative-Decoding on Noureddine RAMDI</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 23 May 2026 20:41:27 +0000</lastBuildDate><atom:link href="https://ramdi.fr/tags/speculative-decoding/index.xml" rel="self" type="application/rss+xml"/><item><title>dflash-mlx: Speculative decoding on Apple Silicon with Metal and MLX</title><link>https://ramdi.fr/github-stars/dflash-mlx-speculative-decoding-on-apple-silicon-with-metal-and-mlx/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/dflash-mlx-speculative-decoding-on-apple-silicon-with-metal-and-mlx/</guid><description>dflash-mlx implements exact speculative decoding for language models on Apple Silicon using Metal and MLX, reducing forward passes with a block-diffusion draft model and per-layer KV cache rollback.</description></item><item><title>Hunting Tokens/sec: 4 LLM Backends, 1 Hard Ceiling (Part 2/4)</title><link>https://ramdi.fr/post/ai-llm/local-llm-tokens-per-second-benchmark/</link><pubDate>Tue, 28 Apr 2026 00:00:00 +0000</pubDate><guid>https://ramdi.fr/post/ai-llm/local-llm-tokens-per-second-benchmark/</guid><description>Part 2 of 4: a benchmark journal across nixpkgs llama.cpp, upstream master, and ik_llama.cpp on Qwen3.6-27B. Six hours, four backends, all converging at 66 tok/s — and the physical reason why.</description></item><item><title>Speculative Decoding Meets Hybrid SSM: Why It Breaks (Part 3/4)</title><link>https://ramdi.fr/post/ai-llm/local-llm-speculative-decoding-hybrid-ssm/</link><pubDate>Tue, 28 Apr 2026 00:00:00 +0000</pubDate><guid>https://ramdi.fr/post/ai-llm/local-llm-speculative-decoding-hybrid-ssm/</guid><description>Part 3 of 4: a deep-dive into why speculative decoding silently breaks (or runs anti-economically) on hybrid attention+SSM architectures like Qwen3.6, Mamba-2, and RWKV — and what would need to change upstream to fix it.</description></item></channel></rss>