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LoongForge is an open‑source, Megatron‑based multimodal training framework that unifies LLM, VLM, VLA and diffusion models, runs seamlessly on NVIDIA GPUs and Baidu Kunlun XPU, and delivers 15%‑45% end‑to‑end training acceleration while scaling linearly on thousands of cards.
The open‑source GLM‑5.1 model, adapted to Baidu Baige's Kunlun XPU via the vLLM‑Kunlun Plugin, delivers record‑breaking SWE‑bench scores, eight‑hour autonomous coding, long‑context handling up to 64K tokens, and scalable deployment across tens of thousands of chips, showcasing end‑to‑end AI acceleration.
Baidu Intelligent Cloud’s seventh‑generation AI confidential virtual machine combines Intel TDX, NVIDIA GPUs, and BlueField DPUs to deliver end‑to‑end encrypted data paths, elastic multi‑GPU scaling, and near‑native performance, proving that high‑sensitivity AI workloads can run securely in the cloud without sacrificing speed.
This article details the technical challenges of adapting the open‑source vLLM inference framework to Baidu's Kunlun XPU, outlines four major performance bottlenecks, and presents a multi‑dimensional optimization roadmap—including custom plugins, operator fusion, INT8 quantization, and CUDA‑Graph techniques—that together boost throughput by up to 8% and narrow the gap with leading GPU hardware.
This article details the vLLM‑Kunlun open‑source project that adapts the high‑performance vLLM inference engine to Baidu's Kunlun XPU, covering platform overview, model‑porting workflow, plugin architecture, concrete case studies with MIMO‑Flash‑V2 and Qwen 3.5, and the performance‑tuning techniques that enable seamless, GPU‑level inference on domestic hardware.
Baidu Baige built a full‑stack quantization pipeline that integrates model‑level, framework‑level, and hardware‑level optimizations on the Kunlun XPU platform, enabling FP16/BF16 large models to be compressed to 25‑50% of their original size while boosting inference speed by 30‑50% and dramatically reducing memory consumption for enterprise deployments.
This article explains how Baidu's new GLM-5 large model is adapted to the Kunlun P800 XPU, detailing the async reinforcement learning framework Slime, optimization techniques like INT8 quantization and tensor‑parallelism, and provides step‑by‑step deployment commands using the open‑source vLLM‑Kunlun plugin.
Baidu’s Baige team successfully adapted the GLM‑4.x series language models to the Kunlun XPU platform by leveraging SGLang and the vLLM‑Kunlun plugin, employing agile adaptation, precision alignment with torch_xray, and extensive performance tuning to achieve GPU‑level accuracy and superior inference speed.
This guide walks through setting up a Kunlun P800 XPU host, preparing Docker containers, deploying Qwen3‑8B/‑32B/‑VL models with vLLM‑Kunlun, benchmarking performance, and running full‑parameter DPO training using LLaMA‑Factory, providing scripts, configuration files, and troubleshooting tips for AI engineers.
LoongFlow is an open‑source AI agent framework that combines a Plan‑Execute‑Summarize (PES) paradigm with a Hybrid Evolutionary Memory system, allowing agents to perform directed, iterative problem solving and achieve state‑of‑the‑art results on mathematical challenges, Kaggle‑style benchmarks, and real‑world tasks with dramatically higher efficiency.
