<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Benchmark on Noureddine RAMDI</title><link>https://ramdi.fr/tags/benchmark/</link><description>Recent content in Benchmark 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/benchmark/index.xml" rel="self" type="application/rss+xml"/><item><title>Understanding Awesome-GraphRAG: A Curated Survey and Benchmark for Graph-Based Retrieval-Augmented Generation</title><link>https://ramdi.fr/github-stars/understanding-awesome-graphrag-a-curated-survey-and-benchmark-for-graph-based-retrieval-augmented-generation/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/understanding-awesome-graphrag-a-curated-survey-and-benchmark-for-graph-based-retrieval-augmented-generation/</guid><description>Awesome-GraphRAG is a curated repository organizing graph-based retrieval-augmented generation methods, with a taxonomy, benchmark, and original research from DEEP-PolyU.</description></item><item><title>SimScale: a scalable sim-real co-training pipeline for autonomous driving planners</title><link>https://ramdi.fr/github-stars/simscale-a-scalable-sim-real-co-training-pipeline-for-autonomous-driving-planners/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/simscale-a-scalable-sim-real-co-training-pipeline-for-autonomous-driving-planners/</guid><description>SimScale provides a sim-real co-training pipeline for autonomous driving planners, combining synthetic simulation data with real-world data to improve robustness and generalization across multiple planner types.</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></channel></rss>