Efficient LLM Deployment on Ascend NPUs: Pangu Embedded & Pro MoE Guide
1 months ago 高效码农Efficient LLM Deployment on Ascend NPUs: Pangu Embedded & Pangu Pro MoE In this post, we explore two complementary solutions from Huawei’s Pangu team—Pangu Embedded and Pangu Pro MoE—designed for low-latency and high-throughput inference on Ascend NPUs. Drawing exclusively on official technical reports, we translate and adapt core concepts into clear, engaging English suitable for junior college–level readers worldwide. We preserve every detail of system design, training methodology, and deployment best practices to deliver genuine, long‑term value without clickbait or hype. Source: Unsplash Table of Contents Why Efficient Inference Matters Pangu Embedded: Fast & Slow Thinking with Metacognition Dual‑System Framework …
From Code to Cloud: How to Deploy Your First LLM App with a Full CI/CD Pipeline
3 months ago 高效码农From Idea to Production: How to Deploy Your First LLM App with a Full CI/CD Pipeline Deployment Workflow Why This Guide Matters Every week, developers ask me: “How do I turn this AI prototype into a real-world application?” Many have working demos in Jupyter notebooks or Hugging Face Spaces but struggle to deploy them as scalable services. This guide bridges that gap using a real-world example: a FastAPI-based image generator powered by Replicate’s Flux model. Follow along to learn how professionals ship AI applications from local code to production. Core Functionality Explained In a Nutshell User submits a text prompt …