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High-Performance Inference Deployment with vLLM Deployment and Operations Intermediate vLLM is currently the most advanced open-source LLM inference engine. This article provides an in-depth explanation of vLLM's PagedAttention principle, production environment configuration, performance tuning strategies, and monitoring solutions to help you build high-throughput inference services. 2025-05 ·18 min read DeepSeek Model Download and Installation Deployment and Operations Beginner Complete Guide to DeepSeek Model Download and Installation: Hugging Face Download, Ollama One-Click Deployment, Quantization Version Selection, and VRAM Requirements Assessment. Includes download links, installation steps, and verification methods for all DeepSeek models. 2025-06 ·18 min read AI Application Observability: Logging, Monitoring, and Alerting in Practice Deployment and Operations Intermediate Production AI applications require a comprehensive observability system. This article details token usage monitoring, API latency tracking, error rate alerts, cost analysis dashboards, and logging best practices to help you build reliable AI services. 2026-08 ·14 min read Inference Engine Internals: Continuous Batching, Prefix Cache, Speculative Decoding Deployment and Operations Advanced Throughput and tail latency pull against each other, and the tension comes down to scheduling and memory. Why static batching wastes capacity, what Continuous Batching and chunked prefill cost to build, how PagedAttention pages memory, when a high Prefix Cache hit rate backfires, and why the acceptance rate of Speculative Decoding sets your speedup. 2026-10 ·76 min read DeepSeek Deployment and Operations Tutorial Deployment and Operations Intermediate Complete DeepSeek model deployment process: vLLM deployment configuration, load balancing, auto-scaling, monitoring and alerting, and cost optimization. Includes Docker and Kubernetes deployment solutions and cloud platform deployment guides, covering all scenarios from single machine to cluster. 2025-06 ·24 min read DeepSeek Performance Tuning Tutorial Deployment and Operations Advanced Complete DeepSeek performance tuning guide: GPU memory optimization, batching strategies, quantization techniques, concurrency control, and latency optimization. Includes profiling tool usage, performance benchmarking, and best practices to achieve an optimal balance between throughput and latency. 2025-06 ·25 min read AI Testing in CI/CD Pipelines Deployment and Operations Intermediate Integrating AI model testing into CI/CD pipelines is key to continuously delivering high-quality AI services. This article explains a complete solution for automated evaluation, regression testing, performance benchmarks, and deployment gates. 2026-07 ·20 min read DeepSeek Inference Acceleration In-Depth Tutorial Deployment and Operations Advanced Comprehensively master DeepSeek model inference acceleration techniques. In-depth comparison of four major engines: vLLM, SGLang, TensorRT-LLM, and llama.cpp, from deployment to tuning, from single GPU to distributed, providing a complete inference acceleration solution. 2025-06 ·25 min read LLM Inference Performance Optimization: vLLM and Continuous Batching Deployment and Operations Advanced After model deployment, throughput and latency are core metrics. This article provides an in-depth analysis of vLLM's PagedAttention and Continuous Batching principles, and gives a complete optimization roadmap from benchmark testing, parameter tuning to production deployment. 2026-08 ·23 min read Panorama of Large Model Inference Acceleration Technologies Deployment and Operations Intermediate Inference speed directly determines user experience. This article systematically explains inference acceleration techniques such as KV Cache, Speculative Decoding, model quantization, and operator fusion, helping you comprehensively master large model inference optimization from principles to practice. 2025-07 ·17 min read DSH Plugin Release, Defensive Programming & Incident Review Deployment and Operations Advanced Three ways to distribute DSH plugins, version conventions, pre-release checks, five defensive patterns, and the dsh incident review discipline for your team. 2026-09 ·39 min read AI Application Cost Engineering: Token Ledgers, Unit Economics, and Cost Governance Deployment and Operations Intermediate Turn a month-end invoice into a daily operational metric. This guide covers a cost event schema and ledgers by feature, tenant and model, cost per session and effective tokens, a unit economics model built from contribution margin, what prompt caching, quantization, small-model routing and batching actually save once corrected for success rate, and nine cost attribution traps. 2026-10 ·30 min read DeepSeek Harness Setup Guide: Install & Configure dsh Deployment and Operations Beginner Run dsh from scratch: npx and global install, Web UI setup, working directory, four run modes, plus DeepSeek, GLM, Qwen, MiMo and Volcengine model config. 2026-09 ·32 min read Plugin Config & Bundle/Profile Layers: How dsh Resolves Effective Config Deployment and Operations Advanced How dsh layers plugin schema defaults, Profile, Bundle, user overrides and CLI patches. Use dsh --dump-config to trace effective values, manifest roles and override priority. 2026-09 ·52 min read Canary Releases for AI Apps: Model, Prompt, and Retrieval Changes Together Deployment and Operations Intermediate When one release changes the model, the prompt, and the retrieval layer at once, attribution gets harder than rollback. This article freezes any request into a content-addressed triple, separates what shadow traffic and A/B splits can and cannot answer, and outlines regression gates covering structure, semantics, retrieval, cost, and latency. 2026-10 ·38 min read Best Practices for Large Model API Service Deployment and Operations Advanced API-fying LLM capabilities is a key step for enterprise-level applications. This article systematically explains best practices for LLM API services from dimensions such as architecture design, rate limiting strategies, cache optimization, and security protection. 2025-06 ·20 min read GPU Cluster Management and Scheduling Deployment and Operations Advanced Managing a cluster of dozens of GPUs requires professional scheduling strategies. This article explains practical experience in GPU resource management, task priority scheduling, memory fragmentation cleanup, and multi-tenant isolation. 2026-07 ·23 min read In-Depth Guide to GPU-Accelerated Inference Optimization Deployment and Operations Advanced GPU inference optimization is key to reducing the cost of large model deployment. This article provides an in-depth explanation of core technologies such as FlashAttention, quantized inference, TensorRT acceleration, and continuous batching to help you maximize GPU utilization. 2025-07 ·19 min read Monitoring and Observability for AI Applications Deployment and Operations Advanced Observability for AI applications is far more complex than for traditional applications—it requires monitoring multiple dimensions such as LLM call latency, token consumption, hallucination rate, and user satisfaction. This article builds a complete AI application monitoring system, covering metric collection, alert configuration, cost tracking, and quality monitoring. 2026-07 ·20 min read Best Practices for Containerizing AI Services Deployment and Operations Intermediate Containerizing AI model services is the first step in production deployment. This article explains a complete solution for Docker image optimization, GPU support, health checks, and Kubernetes deployment. 2026-07 ·22 min read AI API Cost Optimization in Practice Deployment and Operations Intermediate The token cost of AI APIs may seem cheap, but costs can spiral out of control at scale. This article systematically explains practical strategies for optimizing AI API costs, covering prompt compression, semantic caching, model routing, and batch processing, helping you reduce your monthly bill by 50%-80%. 2026-07 ·17 min read DeepSeek Model Selection and Deployment Guide Deployment and Operations Beginner Detailed explanation of the DeepSeek full model series: capability characteristics, applicable scenarios, and deployment solutions for models such as V3, R1, Coder, and VL2. A complete guide from model selection to production deployment, including API calls and local deployment tutorials. 2025-06 ·20 min read Comparison of Local Deployment Options for Large Models Deployment and Operations Beginner Local deployment of large models is a key solution for ensuring data security and controlling costs. This article compares mainstream deployment solutions such as Ollama, vLLM, and llama.cpp, helping you make a choice from dimensions such as ease of use, performance, and hardware requirements. 2025-03 ·15 min read Install DeepSeek Harness: npx, Global npm & Source Build Deployment and Operations Beginner Install DeepSeek Harness three ways: npx, global npm, or source build. Configure dsh web vs headless, Web UI setup, API Key, and OpenAI-compatible endpoints. 2026-09 ·41 min read Complete Guide to Local Deployment of DeepSeek Models Deployment and Operations Intermediate Cloud APIs are great, but many scenarios require local deployment—data security, low latency, and offline use. This article comprehensively explains local deployment solutions for DeepSeek models, covering mainstream tools such as Ollama, vLLM, and llama.cpp, from hardware selection to performance tuning. 2026-07 ·25 min read