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Baidu Cloud's Qianfan platform launched DeepSeek‑R1 and DeepSeek‑V3 with ultra‑low inference pricing, leveraging advanced engine performance tweaks, a split Prefill/Decode architecture, and comprehensive security measures that together boost throughput, cut costs, and ensure enterprise‑grade reliability.
This article analyzes common GPU efficiency problems in enterprise AI compute platforms—such as low utilization, long fault‑resolution times, and limited performance gains—and presents three practical solutions: dynamic resource allocation, systematic fault‑tolerance, and system‑level tuning, illustrated with real‑world case studies.
The article explains iOS sandbox storage by detailing the four main directories, their backup rules, naming conventions, and retrieval APIs, then outlines how to calculate physical file size and implements both automatic quota‑based and manual user‑driven cleaning methods, including system cache removal for tmp, WKWebView, and dyld caches.
The interview explores how Baidu Cloud's Qianfan AppBuilder platform evolves from traditional coding to AI‑native low‑code development, detailing the impact of large‑model agents, Retrieval‑Augmented Generation, security, multimodal support, and future roadmap on enterprise productivity and digital transformation.
Large‑model inference engines convert prompts into responses via a Prefill stage and an autoregressive Decoder, measured by TTFT and TPOT, and Baidu’s AIAK suite improves TPOT by separating tokenization, using static slot scheduling, and asynchronous execution, cutting token‑interval latency from ~35 ms to ~14 ms and boosting GPU utilization to about 75 % while also leveraging quantization and speculative execution for higher throughput.
This roundup highlights twelve standout Baidu Geek articles from 2024, covering breakthroughs in search personalization, high‑performance Go services, transaction reconciliation, login system evolution, AI‑native applications, microservice governance, caching algorithms, RLHF optimization, ClickHouse deployment, and more, each with concise recommendation reasons.
The article outlines how video search ranking has shifted from a tightly‑coupled multi‑stage cascade to an extensible, end‑to‑end, model‑centric framework called Rankflow, leveraging large‑model inference, decoupled recall, fine‑grained parallelism, and elastic compute allocation to boost performance, flexibility, and maintainability while paving the way for future retrieval‑augmented generation integration.
The article presents a MarkupLM‑based approach that enriches BERT with XPath embeddings to jointly model webpage text and structure, enabling site‑level detection of malicious content‑scraping pages that bypass traditional rule‑based filters and demonstrating the critical role of structural cues in improving spam classification accuracy.
Baidu’s Search Content Storage team built an HTAP table storage system and a serverless compute‑scheduling architecture that separates OLTP and OLAP workloads, delivering up to 200 GB/s peak IO, reducing storage cost by 75 %, and enabling SQL‑style task development with native FaaS functions.
This article examines the unique multimodal and multi‑granular nature of web pages, compares fusion strategies, proposes a cross‑modal attention approach, outlines fine‑ and coarse‑grained pre‑training tasks, and explores low‑cost adaptor methods for adapting large multimodal models to web‑page modeling in the LLM era.
