搜索结果：生成式引擎优化

GEO (Generative Engine Optimization) is a systematic strategy to make brand information understood, prioritized, and integrated into AI-generated answers, shifting focus from traditional search rankin

DeepGEMM is a high-performance matrix multiplication library optimized for NVIDIA Hopper GPUs, achieving over 1350 FP8 TFLOPS. It supports standard and Mixture-of-Experts (MoE) computations with just

A 30-minute intermediate-to-advanced guide to mastering WiFi CSI-based pose estimation, deploying both Python and Rust versions, and setting up an ESP32 sensing node to run the end-to-end pipeline. 原文

Laminar is an open-source observability platform for AI agents, offering tracing, evals, monitoring, SQL access, and dashboards. Built with Rust for high performance, it supports OpenTelemetry and integrates with major LLM frameworks. 原文翻译：Laminar是一个面向AI智能体的开源可观测性平台，提供追踪、评估、监控、SQL访问和仪表板功能。基于Rust构建以实现高性能，支持OpenTelemetry，并与主流LLM框架集成。

OpenLIT simplifies AI development with one-line OpenTelemetry-native observability, supporting LLM, vector DB, and GPU monitoring, plus cost tracking and evaluation. 原文翻译：OpenLIT通过一行代码提供OpenTelemetry原生可观测性，简化AI开发，支持LLM、向量数据库和GPU监控，以及成本追踪和评估。

llm-d is a high-performance distributed inference serving stack optimized for production deployments on Kubernetes. It achieves SOTA inference performance across various accelerators by integrating vLLM, Kubernetes Gateway API, and advanced orchestration techniques such as disaggregated serving, prefix-cache aware routing, and tiered KV caching. The v0.5 release demonstrates up to 50k output tok/s on a 16×16 B200 topology. 原文翻译： llm-d是一个针对Kubernetes生产部署优化的高性能分布式推理服务栈。它通过集成vLLM、Kubernetes Gateway API以及分离式推理、前缀缓存感知路由、分层KV缓存等高级编排技术，在各种加速器上实现SOTA推理性能。v0.5版本在16×16 B200拓扑上展示了高达50k输出tok/s的性能。

RAG-Anything is a lightweight RAG system based on LightRAG, designed for multimodal document processing (PDF, images, tables, formulas, etc.). It provides end-to-end parsing, multimodal understanding,

RAG-Anything is an open-source multimodal RAG framework developed by Professor Huang Chao's team at the University of Hong Kong. It builds a unified multimodal knowledge graph architecture to process

RAG-Anything is an all-in-one multimodal RAG system that processes documents containing text, images, tables, and formulas. It features end-to-end processing pipelines, knowledge graph indexing, and c

This paper introduces BlockRank, a method that exploits attention sparsity in LLMs for in-context ranking, reducing complexity from quadratic to linear and enabling efficient retrieval of up to 500 documents within a second. 原文翻译：本文提出BlockRank，利用LLM注意力稀疏性进行上下文排序，将复杂度从二次降至线性，实现秒级检索500个文档。