部署教程
每篇步骤对照官方文档并附来源链接。自己能装，就不必找我们。
对照官方文档的新教程
Ollama · 入门
Ollama 安装与运行第一个本地模型
在 macOS、Windows、Linux 上安装 Ollama，下载并运行一个本地模型，确认是否用上显卡，再用本机接口调用。每一步附官方文档链接。
阅读教程- Ollama安装、模型管理、接口调用、局域网访问已发布 1 篇
- vLLM安装、启动 OpenAI 兼容服务、常用启动参数整理中
- DeepSeek 本地部署模型版本怎么选、用哪种方式跑、许可怎么看整理中
- Dify 部署Docker Compose 部署、接本地模型、建知识库整理中
- 硬件与算力自有显卡、租算力、一体机各适合什么情况整理中
早期文章归档
这些是本站改版前发布的文章，正在逐篇核对与改写；其中的数据请以原始来源为准。
中文文章（200 篇）
- 按调用量算账：OpenAI、Replicate 与自建 vLLM 的 API 成本拆解
- 从 Docker 到生产 API 的完整部署指南：构建可水平扩展的模型推理服务
- 从 Jupyter Notebook 到生产 API：模型部署的工程化鸿沟如何跨越
- 从零构建模型推理 API：Docker、FastAPI 与 vLLM 的组合最佳实践
- 国内用户如何选择海外 GPU 云：RunPod、Lambda Labs 与 Vast.ai 横向评测
- 开源 LLM 生产化部署方案选型：从 Docker 镜像到生产 API 全流程
- 开源模型 API 化部署：使用 vLLM 构建兼容 OpenAI 接口的推理端点
- 模型部署成本控制手册：量化、缓存与请求合并的降本三板斧
- 如何部署开源模型到生产环境：一份涵盖 vLLM、TGI 与 Triton 的实操手册
- 如何构建 AI 推理的成本仪表板：实时追踪每个模型、每个版本的支出
- 如何评估 AI 推理平台的性价比：构建包含延迟、吞吐与成本的综合指标
- 如何评估模型部署方案的总拥有成本：硬件、带宽、运维与机会成本
- 如何为 Agent 应用设计推理基础设施：工具调用、多轮对话与状态管理
- 如何为 RAG 应用部署嵌入与重排序模型的推理服务
- 如何为边缘设备部署推理服务：从云端到 Jetson 的模型适配
- 如何为多租户 SaaS 产品设计推理服务的隔离与计费方案
- 如何为开源 LLM 选择推理框架：vLLM、TGI、Triton 与 Ray Serve 对比
- 如何为开源模型构建与 OpenAI 完全兼容的 API 网关
- 如何为医疗、金融等合规行业部署私有化 AI 推理服务
- 如何选择模型部署的地域：中国大陆、香港、新加坡与美西的延迟测试
- 如何用 vLLM 部署代码生成模型：DeepSeek Coder 的 FIM 推理配置
- 如何用 vLLM 部署多模态模型：LLaVA、Qwen-VL 的推理服务配置
- 如何用 vLLM 部署嵌入模型：从 BGE 到 E5 的文本向量化服务搭建
- 如何用 vLLM 部署嵌入模型和重排序模型为 RAG 管道提速
- 如何用 vLLM 部署语音识别模型：Whisper 的流式与批量推理方案
- 如何用 vLLM 和 FastAPI 构建流式推理端点：SSE 与 WebSocket 实现
- 如何用 vLLM 和 LiteLLM 构建多模型统一 API 网关
- 用 vLLM 部署千问 2.5：从权重下载到 OpenAI 兼容 API 的分步教程
- 自托管 vs Serverless 推理成本对比：以 Llama 3 70B 为例逐项拆解
- 自托管推理的 GPU 温度与功耗监控：Prometheus + NVIDIA DCGM 方案
- 自托管推理的 GPU 虚拟化方案：MIG、vGPU 与时分复用技术选型
- 自托管推理的 SSL 证书自动化：Certbot 与 ACME 协议在私有网络中的应用
- 自托管推理的镜像仓库管理：Harbor、ECR 与安全扫描集成
- 自托管推理的模型热更新：无需重启服务即可切换 LoRA 或基础模型
- 自托管推理方案的备份与灾备：模型权重、配置与日志的高可用设计
- 自托管推理服务的 API 版本管理：如何在不破坏客户端的情况下迭代
- 自托管推理服务的 API 文档自动生成：基于 OpenAPI 与 Swagger 的实现
- 自托管推理服务的 API 限流：令牌桶、滑动窗口与分布式限流实现
- 自托管推理服务的 CI/CD 流水线：模型更新零停机部署的实现
- 自托管推理服务的 TLS 证书管理：Let's Encrypt、Cert-Manager 与自动续签
- 自托管推理服务的压力测试：用 Locust 和 k6 模拟真实用户负载
- 自托管推理服务器搭建实录：从裸金属装机到 vLLM 服务上线
- 自托管推理集群的日志管理：ELK、Loki 与云原生方案的应用
- 自托管推理集群的自动扩缩容：基于 Kubernetes 与 Prometheus 的实现
- AI 部署 SaaS 平台评估清单：安全、合规、SLA 与技术支持怎么考
- AI 模型 A/B 测试部署架构：在 vLLM 后端实现流量分割与金丝雀发布
- AI 模型部署安全清单：API 鉴权、速率限制与模型防盗用策略
- AI 模型部署的 Mock 测试：如何在无 GPU 环境下测试 API 逻辑
- AI 模型部署的容量预留策略：如何保证大促期间的推理资源
- AI 模型部署对比：裸金属、Kubernetes、Serverless 三种架构的适用场景
- AI 模型部署中的成本归因：如何按部门、项目或 API Key 拆分账单
- AI 模型部署中的合规性检查：数据驻留、GDPR 与《个人信息保护法》
- AI 模型部署中的流量预测与容量规划：基于历史数据的自动扩缩容
- AI 模型部署中的模型加密与知识产权保护方案
- AI 推理平台 2026 年综合排名：国内用户如何选择 vLLM、Replicate 与 Modal
- AI 推理平台的供应商锁定风险评估：如何设计可迁移的部署架构
- AI 推理平台的技术支持质量横评：工单响应、社区论坛与文档更新频率
- AI 推理平台的退出策略：如何将模型和数据从平台无缝迁移
- AI 推理平台的性能基准测试框架：构建可重复、可比较的评测标准
- AI 推理平台的灾难恢复演练：模拟区域故障时的切换与恢复流程
- AI 推理平台排行榜：基于吞吐量、成本与易用性的 2026 年综合评分
- AI 推理平台选型决策树：根据模型大小、QPS 与预算快速锁定方案
- AI 推理请求的缓存策略：语义缓存、精确匹配缓存与结果预计算
- AI 推理请求的排队与批处理优化：如何在延迟和吞吐之间取得平衡
- AI 推理延迟优化全景：从网络、序列化到推理引擎的每一毫秒
- GPU 云服务的合同与谈判：大额消费如何争取折扣与专属支持
- GPU 云服务的碳排放考量：选择绿色数据中心的模型部署策略
- GPU 云服务的总拥有成本模型：包含人力、电力、机房与硬件折旧
- GPU 云服务供应商 SLA 对比：正常运行时间、赔偿机制与工单响应速度
- GPU 云服务网络带宽深度评测：跨区域推理对延迟的真实影响
- GPU 云服务选型的最终决策清单：30 个问题帮你锁定最佳平台
- GPU 云服务选型指南：按需付费、包年包月与竞价实例的成本精算
- GPU 云服务选型中的合规与审计：SOC2、ISO27001 与等保认证
- GPU 云服务选型中的区域库存问题：当目标 GPU 售罄时的替代方案
- GPU 云服务隐藏成本揭秘：数据传输、存储快照与静态 IP 的额外费用
- GPU 云服务账单分析与优化：找出闲置资源、重复存储与未释放 IP
- GPU 租赁按小时与按月付费的盈亏平衡点：数学建模与在线计算器
- GPU 租赁避坑指南：竞价实例抢占、区域库存与性能波动应对策略
- GPU 租赁的二手市场与算力转售：合规性、风险与潜在收益
- GPU 租赁的金融化：算力期货、期权与长期合约的定价模型
- GPU 租赁的跨云比价工具：如何一键对比 AWS、GCP、Azure 与独立云厂商
- GPU 租赁的夜间与周末折扣：利用非高峰时段降低批量推理成本
- GPU 租赁的预留实例与节省计划：一年期承诺的折扣到底划不划算
- GPU 租赁市场 2026 年展望：H100、B200 与国产芯片的性价比分析
- GPU 租赁与 Serverless 方案成本精算：从 A100 到 H100 的每小时真实开销
- GPU 租赁长期合约 vs 按需实例：基于稳定推理负载的成本模拟器
- Modal 的 Cron 定时任务功能：如何用 Serverless 实现定期模型评估
- Modal 的 GPU 内存限制与 OOM 处理：如何优雅地捕获并重试
- Modal 的 GPU 时间片调度：短任务如何避免排队并快速完成
- Modal 的 GPU 型号选择：从 T4 到 H100 的性能、价格与适用场景
- Modal 的 Secrets 管理与环境注入：安全传递凭证的标准方法
- Modal 的并行执行模型：如何用 @stub.function 实现数百并发推理
- Modal 的存储卷性能调优：读写带宽、IOPS 与缓存策略的最佳配置
- Modal 的定时任务与工作流：构建每日模型评估与报告生成的自动化管道
- Modal 的跨区域部署：如何在美东、美西和欧洲同时提供服务
- Modal 的实时日志流与调试：如何快速定位推理服务中的异常
- Modal 环境变量与密钥管理：如何安全地注入 API Key 和数据库密码
- Modal 卷快照功能详解：如何将模型加载时间从分钟级缩短到秒级
- Modal 冷启动优化：如何用预热容器和挂载卷降低首字节延迟
- Modal 平台上的 LoRA 热加载：如何实现多租户低成本的模型微服务
- Modal 评测：面向 AI 部署的 Python 原生 Serverless 平台优劣谈
- Modal 上的分布式推理：如何用 MapReduce 模式并行处理大批量请求
- Modal 上的自定义容器部署：如何运行非 Python 语言的推理服务
- Modal 与 AWS Lambda GPU 对比：Python 生态下的 Serverless 推理抉择
- Modal 与 Replicate 的开发者体验对比：文档质量、SDK 易用性与社区活跃度
- Replicate 的 Cog 工具实战：将任意 Python 模型打包为生产级容器
- Replicate 的 Webhook 与异步推理：构建事件驱动的 AI 工作流
- Replicate 的模型安全扫描：如何确保公开模型不含恶意代码
- Replicate 的模型分析面板：调用次数、延迟分布与错误率的解读
- Replicate 的模型共享与团队协作：如何管理组织内的模型访问权限
- Replicate 的模型卡片与文档：如何撰写高质量的模型说明以提升使用量
- Replicate 的模型弃用与下线策略：如何应对依赖模型突然不可用
- Replicate 的模型热修复：如何在不停服的情况下更新模型权重
- Replicate 的模型使用分析：如何通过 API 日志优化模型调用模式
- Replicate 的模型隐私设置：公开、私有与未列出三种可见性详解
- Replicate 的私有端点功能：如何通过 VPC 对等连接保障传输安全
- Replicate 定价模型彻底解析：按秒计费、冷启动与流量成本如何计算
- Replicate 公开模型与私有部署的定价差异：何时该从 API 迁移到自建
- Replicate 模型版本管理与回滚：如何在生产环境中安全更新模型
- Replicate 模型市场分析：哪些公开模型可以直接用于生产环境
- Replicate 训练与微调功能评测：LoRA 训练在云 GPU 上的成本与速度
- Replicate 与 RunPod 成本对比：相同模型在不同平台上的月度账单模拟
- Replicate 中文使用指南：如何通过 Cog 打包并发布自定义模型
- Replicate API 速率限制与重试策略：构建高可用客户端的最佳实践
- RunPod 的 API 与 CLI 工具：如何用脚本自动化管理 GPU 实例
- RunPod 的 Spot 实例使用技巧：如何以三折价格运行非实时推理任务
- RunPod 的发票与税务：中国大陆用户如何获取合规的税务凭证
- RunPod 的启动脚本与初始化：如何自动化配置环境、下载模型与启动服务
- RunPod 的全球节点分布：如何选择离用户最近的机房
- RunPod 的社区生态：第三方工具、模板与自动化脚本盘点
- RunPod 的实例类型选择：社区云、安全云与高可用云的差异
- RunPod 的团队管理：子账号、权限角色与资源配额分配
- RunPod 模板与社区镜像：如何快速启动 Stable Diffusion 与 LLM 实例
- RunPod 企业版功能详解：SSO、审计日志与专属资源组
- RunPod 数据中心网络架构：专线、对等互联与公网带宽的质量
- RunPod 网络存储性能测试：NVMe、HDD 与网络挂载的吞吐量对比
- RunPod 无服务器推理的并发限制与扩容行为：压测数据与官方文档对照
- RunPod 与 Salad 对比：去中心化 GPU 网络与集中式云服务的取舍
- RunPod 与 Vast.ai 对比：社区市场型 GPU 云服务的可靠性与性价比
- RunPod 中文控制台详解：如何用支付宝完成 GPU 实例支付
- RunPod 中文设置与网络优化：中国大陆用户如何获得最低延迟
- RunPod 中文支付与发票问题全解：大陆企业如何合规报销
- RunPod按量计费怎么选：GPU实例配置与停机策略
- RunPod混合计费怎么选：常驻模型与突发推理任务
- Serverless 推理的计费陷阱：最小计费单位、闲置计费与流量费用的真实案例
- Serverless 推理的冷启动缓解策略全景：从预热到快照恢复的工程实践
- Serverless 推理的流量突增应对：冷启动池、预留并发与请求队列机制
- Serverless 推理经济学：当调用量波动巨大时为何选择按需付费
- Serverless 与容器部署的混合架构：何时将流量从 Serverless 切回专用实例
- Serverless GPU 的冷启动时间排行榜：各平台、各型号的启动速度对比
- Serverless GPU 的网络出口费用详解：跨区域传输数据的真实成本
- Serverless GPU 的预留并发与预置容量：确保生产环境零冷启动
- Serverless GPU 冷启动深度剖析：镜像大小、模型加载与网络挂载的影响
- Serverless GPU 冷启动实测：Modal、RunPod 与 Replicate 谁最快响应
- Serverless GPU 平台的 IP 白名单与防火墙：保护推理端点的安全实践
- Serverless GPU 平台的地域延迟测试：从北京、上海、深圳到全球节点的 Ping 值
- Serverless GPU 平台的低价策略对比：免费额度、注册赠金与长期折扣
- Serverless GPU 平台的长期稳定性测试：运行 7 天不间断推理的故障记录
- Serverless GPU 平台选型矩阵：冷启动、最大显存与地域可用区一览
- Serverless GPU 实测：在冷启动与性价比之间找到最佳平衡点
- Serverless GPU 用于批量推理：大规模文本分类、嵌入生成的最佳实践
- Serverless GPU 用于实时语音识别：Whisper 模型部署的成本与延迟实测
- Serverless GPU 用于视频理解：部署 Video-LLaMA 等模型的成本分析
- vLLM 部署常见错误排查：OOM、CUDA 版本冲突与令牌溢出解决方案
- vLLM 部署从入门到生产：如何用 Docker 在单卡上跑通开源大模型
- vLLM 部署的 CPU 与内存需求：除了 GPU 之外还需要多少资源
- vLLM 部署的 Prometheus Exporter 配置：暴露哪些指标，如何设置告警
- vLLM 部署的存储选择：本地 NVMe、网络块存储与对象存储的优劣
- vLLM 部署的多用户隔离：命名空间、资源配额与请求优先级
- vLLM 部署的故障恢复机制：健康检查、自动重启与优雅降级
- vLLM 部署的基准测试方法：用 ShareGPT 和真实流量回放评估性能
- vLLM 部署的监控与可观测性：Prometheus 指标、Grafana 面板与告警规则
- vLLM 部署的启动时间优化：模型预热、内核融合与并行加载技术
- vLLM 部署的日志级别与格式：结构化日志、JSON 输出与日志聚合
- vLLM 部署的容器编排：Kubernetes Deployment、Service 与 Ingress 配置范例
- vLLM 部署的容器化最佳实践：多阶段构建、非 root 用户与只读文件系统
- vLLM 部署的依赖管理：Poetry、Conda 与 Docker 的版本锁定策略
- vLLM 部署教程：在 AWS、阿里云与本地 GPU 集群上配置生产级推理
- vLLM 部署时的网络配置：负载均衡、TLS 终止与 WebSocket 支持
- vLLM 部署中的显存规划：根据模型参数量和序列长度精确计算所需 GPU
- vLLM 的 CUDA Graph 优化：如何通过计算图捕获减少 Kernel Launch 开销
- vLLM 的 FP8 量化在 H100 上的实战：吞吐提升与精度损失的权衡
- vLLM 的 LoRA 适配器管理：动态加载、卸载与多适配器并发服务
- vLLM 的 OpenAI 兼容接口详解：支持哪些参数，有哪些限制
- vLLM 的调度策略解析：先到先服务、优先级队列与公平性保证
- vLLM 的块大小调优：Block Size 对吞吐和显存占用的影响实验
- vLLM 的请求调度可视化：用 Grafana 实时监控队列长度与等待时间
- vLLM 的推测解码实现：用草稿模型将推理速度提升 2 倍
- vLLM 的异步输出处理：当使用流式响应时如何高效处理结果
- vLLM 的长上下文支持：处理 128K Token 输入时的显存与性能调优
- vLLM 对比 TGI：两大开源推理引擎的吞吐量与易用性较量
- vLLM 多卡并行部署：张量并行、流水线并行与数据并行的配置详解
- vLLM 量化部署指南：AWQ、GPTQ 与 FP8 在不同 GPU 上的性能实测
- vLLM 前缀缓存原理与实战：如何让长对话推理成本降低一半
- vLLM 生产环境调优：连续批处理、PagedAttention 与量化策略实战
- vLLM 与 OpenLLM 对比：两个开源部署框架的设计哲学与适用场景
- vLLM 与 Replicate 深度对比：延迟、吞吐量与长期总拥有成本分析
- vLLM 与 SGLang 对比：下一代推理框架在调度算法上的创新
- vLLM 与 TensorRT-LLM 对比：NVIDIA 生态下的推理引擎终极对决
- vLLM 在消费级显卡上的部署：RTX 4090 运行 7B 模型的极限调优
English articles (200)
- A Full Spectrum of Cold Start Mitigation Strategies for Serverless Inference: From Warming to Snapshot Restoration
- A Performance Benchmarking Framework for AI Inference Platforms: Building Repeatable and Comparable Evaluation Standards
- A/B Testing Deployment Architecture for AI Models: Traffic Splitting and Canary Releases on a vLLM Backend
- AI Deployment SaaS Evaluation Checklist: Security, Compliance, SLA, and Technical Support
- AI Inference Platform Decision Tree: Quickly Lock in a Solution by Model Size, QPS, and Budget
- AI Inference Platform Leaderboard: A 2026 Composite Score Based on Throughput, Cost, and Usability
- AI Inference Platform Rankings 2026: vLLM vs Replicate vs Modal for Global Teams
- AI Model Deployment Comparison: Bare Metal, Kubernetes, and Serverless Architectures
- AI Model Deployment Security Checklist: API Authentication, Rate Limiting, and Model Theft Prevention
- API Cost Accounting by Call Volume: Comparing OpenAI, Replicate, and Self-Hosted vLLM
- API Rate Limiting for Self-Hosted Inference Services: Token Bucket, Sliding Window, and Distributed Implementations
- API Version Management for Self-Hosted Inference: Iterating Without Breaking Client Applications
- API-Fying Open-Source Models: Building an OpenAI-Compatible Endpoint with vLLM
- Auto-Generating API Documentation for Self-Hosted Inference: An Implementation with OpenAPI and Swagger
- Auto-Scaling a Self-Hosted Inference Cluster: Implementation with Kubernetes and Prometheus
- Backup and Disaster Recovery for Self-Hosted Inference: High Availability Design for Weights, Config, and Logs
- Benchmarking Methodology for vLLM Deployments: Performance Evaluation with ShareGPT and Real Traffic Replay
- Building a Model Inference API from Scratch: Best Practices with Docker, FastAPI, and vLLM
- Building a Self-Hosted Inference Server: From Bare Metal Setup to vLLM Service Launch
- Caching Strategies for AI Inference Requests: Semantic Cache, Exact Match Cache, and Result Precomputation
- Capacity Reservation Strategies for AI Model Deployment: Ensuring Inference Resources During Peak Seasons
- Carbon Emissions Considerations for GPU Cloud: Model Deployment Strategies for Choosing Green Data Centers
- CI/CD Pipelines for Self-Hosted Inference Services: Achieving Zero-Downtime Model Updates
- Common vLLM Deployment Errors and Fixes: OOM, CUDA Version Conflicts, and Token Overflow Solutions
- Compliance and Audit in GPU Cloud Selection: SOC2, ISO27001, and Global Certifications
- Compliance in AI Model Deployment: Data Residency, GDPR, and Global Privacy Regulations
- Container Orchestration for vLLM Deployment: Kubernetes Deployment, Service, and Ingress Configuration Examples
- Containerization Best Practices for vLLM: Multi-Stage Builds, Non-Root Users, and Read-Only Filesystems
- Cost Attribution in AI Model Deployment: Splitting Bills by Department, Project, or API Key
- CPU and Memory Requirements for vLLM Deployment: What Resources Are Needed Beyond the GPU
- Cross-Cloud Price Comparison Tools for GPU Rental: One-Click Comparison of AWS, GCP, Azure, and Independent Clouds
- Custom Container Deployment on Modal: Running Non-Python Inference Services
- Dependency Management for vLLM Deployment: Version Locking Strategies with Poetry, Conda, and Docker
- Deploying Qwen 2.5 with vLLM: A Step-by-Step Tutorial from Weight Download to OpenAI-Compatible API
- Disaster Recovery Drills for AI Inference Platforms: Simulating Regional Failures and Switchover Processes
- Distributed Inference on Modal: Processing Large Batches in Parallel Using the MapReduce Pattern
- Exit Strategy for AI Inference Platforms: Seamlessly Migrating Models and Data Off a Platform
- Fault Recovery Mechanisms for vLLM Deployments: Health Checks, Auto-Restart, and Graceful Degradation
- FP8 Quantization on H100 with vLLM in Practice: The Trade-Off Between Throughput Gain and Precision Loss
- From Docker to Production API: Building a Horizontally Scalable Model Inference Service
- From Jupyter Notebook to Production API: Bridging the Engineering Gap in Model Deployment
- GPU Cloud Bill Analysis and Optimization: Finding Idle Resources, Duplicate Storage, and Unreleased IPs
- GPU Cloud Contracts and Negotiation: How to Secure Discounts and Dedicated Support for Large Spending
- GPU Cloud Hidden Costs Revealed: Data Transfer, Storage Snapshots, and Static IP Extra Charges
- GPU Cloud Network Bandwidth Deep Dive: The Real Impact of Cross-Region Inference on Latency
- GPU Cloud Provider SLA Comparison: Uptime Guarantees, Compensation Mechanisms, and Ticket Response Speed
- GPU Cloud Service Selection: Comparing On-Demand, Reserved, and Spot Instance Costs
- GPU Rental Long-Term Contract vs On-Demand: A Cost Simulator for Stable Inference Workloads
- GPU Rental Market Outlook 2026: Cost-Efficiency Analysis of H100, B200, and Emerging Chips
- GPU Rental Pitfalls to Avoid: Spot Instance Preemption, Regional Stock, and Performance Fluctuations
- GPU Rental vs Serverless Cost Calculation: Real Hourly Expenses from A100 to H100
- GPU Temperature and Power Monitoring for Self-Hosted Inference: A Prometheus + NVIDIA DCGM Solution
- GPU Virtualization for Self-Hosted Inference: MIG, vGPU, and Time-Sharing Technology Options
- Handling Traffic Spikes with Serverless Inference: Cold Start Pools, Reserved Concurrency, and Request Queues
- Hot Model Reloading for Self-Hosted Inference: Switching LoRA or Base Models Without Service Restart
- How to Build a Cost Dashboard for AI Inference: Tracking Spending Per Model and Version in Real Time
- How to Build a Multi-Model Unified API Gateway with vLLM and LiteLLM
- How to Build a Streaming Inference Endpoint with vLLM and FastAPI: SSE and WebSocket Implementation
- How to Build an OpenAI-Fully-Compatible API Gateway for Open-Source Models
- How to Choose a Deployment Region: Latency Tests from North America, Europe, and Asia-Pacific
- How to Choose an Inference Framework for Open-Source LLMs: Comparing vLLM, TGI, Triton, and Ray Serve
- How to Choose an Overseas GPU Cloud: A Horizontal Review of RunPod, Lambda Labs, and Vast.ai
- How to Deploy Code Generation Models with vLLM: FIM Inference Configuration for DeepSeek Coder
- How to Deploy Embedding and Reranking Models for RAG Applications
- How to Deploy Embedding Models with vLLM: Building Text Vectorization Services from BGE to E5
- How to Deploy Inference Services for Edge Devices: Model Adaptation from Cloud to Jetson
- How to Deploy Multimodal Models with vLLM: Inference Service Configuration for LLaVA and Qwen-VL
- How to Deploy Open-Source Models to Production: A Practical Handbook Covering vLLM, TGI, and Triton
- How to Deploy Private AI Inference Services for Regulated Industries like Healthcare and Finance
- How to Deploy Speech Recognition Models with vLLM: Streaming and Batch Inference Solutions for Whisper
- How to Design Inference Infrastructure for Agent Applications: Tool Calling, Multi-Turn Dialogue, and State Management
- How to Design Isolation and Billing for Multi-Tenant SaaS Inference Services
- How to Evaluate the Price-Performance Ratio of AI Inference Platforms: A Composite Metric with Latency, Throughput, and Cost
- How to Evaluate the Total Cost of Ownership for Model Deployment: Hardware, Bandwidth, Operations, and Opportunity Cost
- How to Speed Up RAG Pipelines by Deploying Embedding and Reranking Models with vLLM
- Hybrid Architecture of Serverless and Container Deployments: When to Shift Traffic Back to Dedicated Instances
- Image Registry Management for Self-Hosted Inference: Integrating Harbor, ECR, and Security Scanning
- IP Whitelisting and Firewalling for Serverless GPU Platforms: Security Practices to Protect Endpoints
- Log Management for Self-Hosted Inference Clusters: Applying ELK, Loki, and Cloud-Native Solutions
- Long-Term Stability Test of Serverless GPU Platforms: A 7-Day Uninterrupted Inference Failure Log
- LoRA Hot-Loading on Modal: Building Cost-Effective Multi-Tenant Model Microservices
- Low-Price Strategies of Serverless GPU Platforms: Free Tiers, Sign-Up Credits, and Long-Term Discounts
- Mock Testing for AI Model Deployment: Testing API Logic Without a GPU Environment
- Modal Cold Start Optimization: Reducing Time to First Byte with Warm Containers and Mounted Volumes
- Modal Cron Job Feature: Automating Periodic Model Evaluation with Serverless
- Modal Cross-Region Deployment: Serving Traffic Simultaneously from Multiple Global Locations
- Modal Environment Variables and Secrets Management: Securely Injecting API Keys and Database Passwords
- Modal GPU Memory Limits and OOM Handling: Gracefully Catching and Retrying
- Modal GPU Model Selection: Performance, Pricing, and Use Cases from T4 to H100
- Modal GPU Time-Slice Scheduling: How Short Tasks Avoid Queuing and Complete Quickly
- Modal Parallel Execution Model: Achieving Hundreds of Concurrent Inferences with @stub.function
- Modal Real-Time Log Streaming and Debugging: Quickly Locating Anomalies in Inference Services
- Modal Review: The Pros and Cons of a Python-Native Serverless Platform for AI Deployment
- Modal Scheduled Tasks and Workflows: Building an Automated Pipeline for Daily Model Evaluation and Reporting
- Modal Secrets Management and Environment Injection: Standard Methods for Passing Credentials Securely
- Modal Storage Volume Performance Tuning: Best Configuration for Read/Write Bandwidth, IOPS, and Caching
- Modal Volume Snapshots Explained: Reducing Model Loading Time from Minutes to Seconds
- Modal vs AWS Lambda GPU: Choosing Serverless Inference in the Python Ecosystem
- Modal vs Replicate Developer Experience: Documentation Quality, SDK Usability, and Community Activity
- Model Deployment Cost Control Handbook: Quantization, Caching, and Request Batching
- Model Encryption and Intellectual Property Protection in AI Model Deployment
- Monitoring and Observability for vLLM Deployments: Prometheus Metrics, Grafana Dashboards, and Alerting Rules
- Multi-User Isolation for vLLM Deployment: Namespaces, Resource Quotas, and Request Prioritization
- Network Configuration for vLLM Deployment: Load Balancing, TLS Termination, and WebSocket Support
- Night and Weekend Discounts for GPU Rental: Cutting Batch Inference Costs Using Off-Peak Hours
- Open-Source LLM Production Deployment: A Full Guide from Docker Image to API Endpoint
- Optimizing AI Inference Request Queuing and Batching: Balancing Latency and Throughput
- Prometheus Exporter Configuration for vLLM: Which Metrics to Expose and How to Set Up Alerts
- Regional Stock Issues in GPU Cloud Selection: Alternatives When the Target GPU Is Sold Out
- Replicate API Rate Limiting and Retry Strategies: Best Practices for Building a Highly Available Client
- Replicate Cog Tool in Practice: Packaging Any Python Model into a Production-Grade Container
- Replicate Model Analytics Dashboard: Interpreting Call Volume, Latency Distribution, and Error Rates
- Replicate Model Cards and Documentation: Writing High-Quality Model Descriptions to Boost Usage
- Replicate Model Deprecation and Sunset Policy: How to Handle the Sudden Unavailability of a Dependency
- Replicate Model Hotfix: Updating Model Weights Without Service Downtime
- Replicate Model Marketplace Analysis: Which Public Models Are Ready for Production
- Replicate Model Privacy Settings: Public, Private, and Unlisted Visibility Explained
- Replicate Model Security Scanning: Ensuring Public Models Are Free of Malicious Code
- Replicate Model Sharing and Team Collaboration: Managing Model Access Within an Organization
- Replicate Model Usage Analytics: Optimizing Call Patterns Through API Log Analysis
- Replicate Model Versioning and Rollback: Safely Updating Models in a Production Environment
- Replicate Pricing Model Fully Explained: Per-Second Billing, Cold Starts, and Data Transfer Costs
- Replicate Private Endpoint Feature: Securing Data Transmission via VPC Peering
- Replicate Public Models vs Private Deployment Pricing: When to Migrate from API to Self-Hosting
- Replicate Training and Fine-Tuning Review: Cost and Speed of LoRA Training on Cloud GPUs
- Replicate User Guide: How to Package and Publish Custom Models Using Cog
- Replicate vs RunPod Cost Comparison: Monthly Bill Simulation for the Same Model on Different Platforms
- Replicate Webhooks and Asynchronous Inference: Building Event-Driven AI Workflows
- Reserved Concurrency and Provisioned Capacity for Serverless GPU: Ensuring Zero Cold Starts in Production
- Reserved Instances and Savings Plans for GPU Rental: Are 1-Year Commitment Discounts Worth It
- RunPod API and CLI Tools: Automating GPU Instance Management with Scripts
- RunPod Billing and Invoicing for International Users: A Complete Guide to Compliance and Taxation
- RunPod Community Ecosystem: A Roundup of Third-Party Tools, Templates, and Automation Scripts
- RunPod Console and Payment Methods Explained: A Guide for International Users
- RunPod Data Center Network Architecture: Quality of Private Lines, Peering, and Public Bandwidth
- RunPod Enterprise Features Explained: SSO, Audit Logs, and Dedicated Resource Groups
- RunPod Global Node Distribution: How to Choose the Data Center Closest to Your Users
- RunPod Instance Type Selection: Differences Between Community Cloud, Secure Cloud, and High Availability Cloud
- RunPod Invoicing and Tax: How International Users Obtain Compliant Tax Documentation
- RunPod Network Optimization for Global Users: Achieving the Lowest Latency Worldwide
- RunPod Network Storage Performance Test: Throughput Comparison of NVMe, HDD, and Network Volumes
- RunPod Pay-Per-Use and Monthly Instance Mix: A Cost-Saving Combo for Base and Burst Loads
- RunPod Serverless Concurrency Limits and Scaling Behavior: Stress Test Data vs Official Documentation
- RunPod Serverless GPU In-Depth Review: How Much Can You Really Save with Per-Second Billing
- RunPod Spot Instance Tips: Running Non-Real-Time Inference Tasks at a 70% Discount
- RunPod Startup Scripts and Initialization: Automating Environment Setup, Model Downloads, and Service Launch
- RunPod Team Management: Sub-Accounts, Permission Roles, and Resource Quota Allocation
- RunPod Templates and Community Images: Quickly Launching Stable Diffusion and LLM Instances
- RunPod vs Salad: Trade-Offs Between Decentralized GPU Networks and Centralized Cloud Services
- RunPod vs Vast.ai: Reliability and Cost-Effectiveness of Community Marketplace GPU Clouds
- Self-Hosted vs Serverless Inference Cost: A Line-by-Line Breakdown with Llama 3 70B
- Serverless GPU Cold Start Benchmarks: Modal, RunPod, and Replicate Response Times Tested
- Serverless GPU Cold Start Deep Analysis: Impact of Image Size, Model Loading, and Network Attachment
- Serverless GPU Cold Start Time Leaderboard: Startup Speed Comparison Across Platforms and GPU Models
- Serverless GPU for Batch Inference: Best Practices for Large-Scale Text Classification and Embedding Generation
- Serverless GPU for Real-Time Speech Recognition: Cost and Latency Benchmarks for Deploying Whisper
- Serverless GPU for Video Understanding: Cost Analysis for Deploying Models like Video-LLaMA
- Serverless GPU Network Egress Fees Explained: The True Cost of Cross-Region Data Transfer
- Serverless GPU Platform Latency Test by Region: Ping Values from Major Global Cities to Cloud Nodes
- Serverless GPU Platform Selection Matrix: Cold Start, Max VRAM, and Regional Availability at a Glance
- Serverless GPU Tested in Practice: Finding the Sweet Spot Between Cold Start and Cost-Efficiency
- Serverless Inference Billing Traps: Real Cases of Minimum Billing Units, Idle Charges, and Data Transfer Fees
- SSL Certificate Automation for Self-Hosted Inference: Certbot and ACME Protocol in Private Networks
- Startup Time Optimization for vLLM Deployment: Model Warm-Up, Kernel Fusion, and Parallel Loading Techniques
- Storage Choices for vLLM Deployment: Local NVMe, Network Block Storage, and Object Storage Compared
- Stress Testing Self-Hosted Inference Services: Simulating Real User Load with Locust and k6
- Technical Support Quality Review for AI Inference Platforms: Ticket Response, Community Forums, and Doc Updates
- The Break-Even Point for GPU Rental Hourly vs Monthly Billing: Mathematical Modeling and an Online Calculator
- The Economics of Serverless Inference: Why Pay-Per-Use Wins When Traffic Is Highly Variable
- The Financialization of GPU Rental: Pricing Models for Compute Futures, Options, and Long-Term Contracts
- The Full Picture of AI Inference Latency Optimization: Every Millisecond from Network to Inference Engine
- The Second-Hand Market and Compute Reselling for GPU Rental: Compliance, Risks, and Potential Returns
- The Total Cost of Ownership Model for GPU Cloud: Including Labor, Power, Colocation, and Hardware Depreciation
- The Ultimate Decision Checklist for GPU Cloud Selection: 30 Questions to Lock in the Best Platform
- TLS Certificate Management for Self-Hosted Inference: Let's Encrypt, Cert-Manager, and Auto-Renewal
- Traffic Prediction and Capacity Planning for AI Model Deployment: Auto-Scaling Based on Historical Data
- Vendor Lock-In Risk Assessment for AI Inference Platforms: Designing a Migratable Deployment Architecture
- vLLM Asynchronous Output Handling: Efficiently Processing Results with Streaming Responses
- vLLM Block Size Tuning: An Experiment on the Impact of Block Size on Throughput and VRAM Usage
- vLLM CUDA Graph Optimization: Reducing Kernel Launch Overhead with Computation Graph Capture
- vLLM Deployment Guide: From Docker to Production API on a Single GPU
- vLLM Deployment Tutorial: Configuring Production-Grade Inference on AWS, GCP, and On-Premises Clusters
- vLLM Logging Levels and Formats: Structured Logging, JSON Output, and Log Aggregation
- vLLM Long Context Support: VRAM and Performance Tuning for Processing 128K Token Inputs
- vLLM LoRA Adapter Management: Dynamic Loading, Unloading, and Concurrent Serving of Multiple Adapters
- vLLM Multi-GPU Deployment: A Detailed Configuration Guide for Tensor, Pipeline, and Data Parallelism
- vLLM on Consumer GPUs: Extreme Tuning for Running 7B Models on an RTX 4090
- vLLM OpenAI-Compatible API in Detail: Supported Parameters and Limitations
- vLLM Prefix Caching in Practice: How to Halve the Cost of Long Conversation Inference
- vLLM Production Tuning: Continuous Batching, PagedAttention, and Quantization Strategies in Action
- vLLM Quantization Deployment Guide: AWQ, GPTQ, and FP8 Performance Benchmarked on Different GPUs
- vLLM Request Scheduling Visualization: Real-Time Monitoring of Queue Length and Wait Time with Grafana
- vLLM Scheduling Policy Explained: First-Come-First-Served, Priority Queues, and Fairness Guarantees
- vLLM Speculative Decoding Implementation: Doubling Inference Speed with a Draft Model
- vLLM vs OpenLLM: Design Philosophy and Use Cases of Two Open-Source Deployment Frameworks
- vLLM vs Replicate Deep Dive: Latency, Throughput, and Total Cost of Ownership Analysis
- vLLM vs SGLang: Innovations in Scheduling Algorithms for Next-Gen Inference Frameworks
- vLLM vs TensorRT-LLM: The Ultimate Showdown of Inference Engines in the NVIDIA Ecosystem
- vLLM vs TGI: A Battle of Throughput and Usability Between Two Leading Open-Source Engines
- VRAM Planning for vLLM Deployment: Precisely Calculating GPU Needs by Model Parameters and Sequence Length