Architecture

Introduction vLLM is an open-source, production-focused inference engine for large language models (LLMs) that prioritizes high throughput, low latency, and efficient GPU memory usage. This post provides a deep technical dive into vLLM’s architecture, core innovations (especially PagedAttention), quantization and model support, scheduling and batching strategies, distributed and multi-GPU operation, practical deployment patterns, benchmarks and trade-offs, and troubleshooting tips for production systems. Table of contents Introduction What is vLLM and when to use it Core innovations PagedAttention and KV memory management Micro-batching and continuous batching Kernel and CUDA optimizations Model support and quantization Supported model families and formats Quantization: GPTQ, AWQ, INT4/INT8/FP8 Scheduling, batching, and token routing Multi-GPU and distributed inference Tensor and pipeline parallelism MoE and expert routing considerations Integration and developer experience Hugging Face and OpenAI-compatible APIs Example: simple Python server invocation Production deployment patterns Cost and utilization considerations Scaling strategies and failure isolation Benchmarks, comparisons, and trade-offs vLLM vs alternatives (TensorRT‑LLM, LMDeploy, SGLang, Transformers) Common issues and operational tips Conclusion What is vLLM and when to use it vLLM is a high-performance inference engine designed to serve transformer-based LLMs with high concurrency and long context windows while keeping GPU memory usage efficient. Use vLLM when you need to serve many concurrent users or large contexts with good throughput, when you want easy integration with Hugging Face models, and when maximizing GPU utilization (through micro-batching and efficient KV caching) is a priority[4][1]. ...

Introduction RSS and Atom feeds remain foundational for syndicating content across the web—from news and blogs to podcasts and enterprise integrations. Designing a robust feed system isn’t just about outputting XML; it’s about correctness, scale, freshness, discoverability, compatibility, and reliability. This article walks through a detailed system design for building and operating RSS/Atom feeds. We’ll cover data modeling, HTTP semantics, caching, pagination and archiving, push (WebSub) vs pull, security, observability, and practical implementation snippets. A comprehensive Resources section at the end provides standards, validators, and production-ready libraries. ...

Introduction Distributed systems run everything from streaming platforms to payment networks and logistics providers. Building them for production requires more than just connecting services—you need to understand failure modes, consistency models, data and network behavior, and how to operate systems reliably at scale. This article provides a high-level but comprehensive tour of the essential concepts you need in practice. It favors pragmatic guidance, proven patterns, and the “gotchas” teams hit in real-world environments. ...

Introduction Imagine a doorbell. You press it (something happens), the chime sounds (a reaction happens), and perhaps a camera starts recording (another reaction). You don’t call the chime function directly. You signal that “an event occurred,” and any number of listeners react. That’s the core of events in software: something happens, interested parties respond. Events are everywhere—GUI buttons, network sockets becoming readable, a file changing, a business action like “order_placed,” or a job finishing. In Python, you can use events via libraries (Tkinter, Qt, asyncio, Django signals), operating-system interfaces (selectors), or create your own event systems. ...

Introduction Command Query Responsibility Segregation (CQRS) is an architectural pattern that separates reads (queries) from writes (commands). Rather than using a single data model and layer to both modify and read state, CQRS encourages designing optimized models and pathways for each. This separation can improve scalability, performance, and clarity—especially in complex domains—while introducing new challenges around consistency, messaging, and operational complexity. This guide provides a practical, vendor-neutral overview of CQRS: what it is, when it helps, how to implement it (with and without event sourcing), and the pitfalls to avoid. Code examples are provided to illustrate implementation techniques. ...