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vLLM에 요청 하나가 들어와 토큰이 나오기까지의 전체 경로를 따라갑니다. API 서버, 스케줄러, KV 캐시 매니저, 워커, 샘플러가 각각 무슨 일을 하는지, V1 재작성 이후 프로세스가 어떻게 나뉘는지를 공식 문서와 소스로 확인해 정리했습니다. vLLM 내부 구조 시리즈 1편.
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- Published on
LLM 애플리케이션 스택은 추론 서버, 오케스트레이션, 게이트웨이, 에이전트, RAG로 층이 갈라졌습니다. 각 층에서 실제로 쓰이는 오픈소스 12개를 스타 순위가 아니라 역할별로 묶어 소개합니다. 프로젝트마다 무엇을 대체하는지, 성숙도가 어느 정도인지, 어떤 상황에서 쓰면 안 되는지를 함께 정리했고, 저장소 경로와 라이선스, 스타 수, 최근 푸시 날짜는 2026년 8월 12일 GitHub에서 직접 확인한 값을 그대로 적었습니다. Open WebUI의 브랜딩 제한 조항처럼 OSI 승인 오픈소스가 아닌 경우도 숨기지 않고 표시합니다.- Published on
A practical comparison of AI gateway layers as of 2026-04-12, showing where Vercel AI Gateway, Cloudflare AI Gateway, and Amazon Bedrock AgentCore Gateway belong in the stack.- Published on
2025년 12월 22일 공개된 Vercel AI SDK 6와 AI Gateway를 바탕으로, 2026년 멀티 모델 아키텍처를 설계하는 방법과 fallback, provider routing, human approval, Next.js 도입 체크리스트를 실무 관점에서 정리한다.- Published on
A practical guide to Amazon Bedrock AgentCore for teams that need secure, production-ready agents, with clear coverage of Runtime, Memory, Gateway, observability, and rollout checks.- Published on
Azure AI Foundry Agent Service를 엔터프라이즈 관점에서 정리한 실전 가이드로, 왜 관리형 에이전트가 필요한지, 도구 카탈로그와 원격 MCP 서버를 어떻게 활용하는지, 그리고 tracing, evaluation, governance, private networking 기준으로 어떻게 배포를 판단할지 설명한다.- Published on
브라우저와 가상 컴퓨터를 직접 조작하는 에이전트가 왜 지금 중요해졌는지, 어떤 아키텍처로 설계해야 하는지, 어디까지 자동화하고 어디서 멈춰야 하는지 실무 기준으로 정리한다.- Published on
Cloudflare AI Gateway를 왜 쓰는지, 어떤 제어가 가능한지, Dynamic Routing과 자동 재시도를 어떻게 함께 써야 하는지 2026년 4월 기준으로 실무적으로 정리한다.- Published on
2026년 시점에서 Gemini CLI가 IDE 우선 도구와 어떻게 다른지, plan mode와 hooks, MCP, 스크립팅이 실제 개발 흐름에 어떤 의미를 갖는지, 그리고 팀에 안전하게 도입하는 방법을 정리한다.- Published on
Google Agent Development Kit를 평가하는 팀을 위한 실전 가이드로, 컨텍스트 관리, callbacks, 멀티에이전트 구성, 롤아웃 판단 기준을 중심으로 정리한다.- Published on
LlamaIndex Workflows를 이벤트 기반 설계, observability, human-in-the-loop, LlamaDeploy 관점에서 정리한 실전 가이드입니다. 언제 쓰고 어떻게 운영에 올릴지까지 함께 다룹니다.- Published on
A practical comparison of three managed agent platforms as of 2026-04-12, including product fit, governance, tooling, deployment, and rollout checklists grounded in official docs.- Published on
오픈소스 TypeScript 스택 안에서 에이전트, 메모리, 워크플로, 관측 가능성, 평가, 프로덕션 배포를 함께 다뤄야 하는 팀을 위한 Mastra 실전 가이드입니다.- Published on
2025년 10월 6일 공개된 OpenAI AgentKit을 기준으로, 데이터셋 기반 평가와 트레이스 그레이딩, 자동 프롬프트 최적화를 어떻게 운영 워크플로에 연결할지 실무 관점에서 정리한다.- Published on
2025년 3월 11일 공개된 OpenAI Responses API와 Agents SDK를 기준으로, 어떤 팀이 Chat Completions를 유지하고 어떤 팀이 Responses API로 옮겨야 하는지, Assistants API 사용자는 무엇을 언제 준비해야 하는지 실무 중심으로 정리한다.- Published on
A practical guide to OpenAI reinforcement fine-tuning with custom graders, including when to use it, how to prepare data, how to evaluate checkpoints, and how to roll it out safely.- Published on
Python 중심 에이전트 시스템, 모델 유연성, 내구성 있는 워크플로, 관측 가능성, 평가 체계가 필요한 팀을 위한 PydanticAI 실전 가이드입니다.- Published on
Model Context Protocol을 실무에 도입할 때 필요한 서버 경계 설정, tool 설계, transport 선택, 인증, 토큰 예산 관리, 운영 체크리스트를 공식 문서 중심으로 정리합니다.- Published on
2026년 3월 Anthropic이 Claude Opus 4.6/Sonnet 4.6의 1M 토큰 컨텍스트 윈도우를 GA로 발표했다. 기존 128K~200K 제한에서 1M으로의 확장이 가져오는 활용 패러다임의 전환, 실전 활용 패턴 5가지, RAG 대비 트레이드오프, 비용 최적화 전략까지 종합 가이드를 제공한다.- Published on
단일 에이전트에서 멀티에이전트 협업까지 — 계층형, 파이프라인, 스웜 패턴의 설계 원칙과 LangGraph·CrewAI·AutoGen 프레임워크별 구현 방법을 실전 코드와 함께 정리한다.- Published on
2026년 AI 코딩 에이전트의 자율성이 높아지면서 보안 격리가 필수가 되었습니다. macOS sandbox-exec, 컨테이너 기반 격리, MicroVM 등 실전 샌드박싱 기술과 운영 가이드를 다룹니다.- Published on
Staff/Principal Engineer의 역할 정의, 기술 의사결정(RFC/ADR), 영향력 확장, 멘토링, 기술 부채 관리, 엔지니어링 문화 구축 등 시니어 이상 엔지니어의 커리어 성장 전략을 다룹니다.- Published on
Andrej Karpathy가 제안한 Vibe Coding 개념의 1년 후 현실을 분석합니다. 2026년 AI 코딩 도구 생태계의 현황, 생산성 데이터, 한계, 그리고 엔지니어가 준비해야 할 전략을 다룹니다.- Published on
LLMOps 플랫폼의 설계와 구현을 다룹니다. vLLM/TGI 기반 모델 서빙, 토큰 사용량/레이턴시/품질 모니터링, 프롬프트 버전 관리, A/B 테스트 프레임워크, 가드레일 통합까지 프로덕션 LLM 운영의 전체 라이프사이클을 코드와 함께 구축합니다.- Published on
Feature Store의 핵심 개념(Online/Offline Serving, Feature Freshness, Point-in-Time Correctness)부터 Feast 아키텍처, Feature 정의와 Entity 설계, Materialization 파이프라인, Online Store 백엔드(Redis, DynamoDB), Offline Store(BigQuery, Redshift), Training-Serving Skew 방지, Feature Monitoring과 Drift Detection, Tecton/Hopsworks와의 비교, 프로덕션 배포 패턴까지 다룹니다.- Published on
KServe를 활용한 Kubernetes 기반 모델 서빙을 다룹니다. InferenceService CRD로 모델 배포, Canary 전략으로 안전한 롤아웃, Transformer로 전후처리 파이프라인, InferenceGraph로 DAG 기반 복합 추론까지 프로덕션 운영 전략을 코드와 함께 구현합니다.- Published on
Kubeflow Pipelines를 활용한 ML 워크플로우 오케스트레이션을 실전 중심으로 다룹니다. KFP v2 SDK 아키텍처, 파이프라인 컴포넌트 작성, 캐싱 전략, Argo Workflows/Airflow 비교, 장애 대응까지 프로덕션 환경에서 필요한 전략을 상세히 설명합니다.- Published on
MLflow를 활용한 ML 실험 추적, 모델 레지스트리, 배포 파이프라인을 실전 중심으로 다룹니다. Tracking Server 아키텍처부터 자동 로깅, 모델 버전 관리, Kubernetes/Docker 배포까지 프로덕션 환경에서 필요한 MLOps 전략을 상세히 설명합니다.- Published on
ML 시스템의 핵심 인프라인 Feature Store를 심층적으로 다룹니다. Feast 프레임워크의 아키텍처와 구현, 온라인/오프라인 피처 서빙, 피처 엔지니어링 파이프라인 통합, Tecton 비교 분석, 프로덕션 운영 노하우까지 제공합니다.- Published on
LLM 프로덕션 모니터링 플랫폼 3종(LangSmith, LangFuse, Arize Phoenix) 종합 비교 가이드. 트레이스 수집, 프롬프트 버전 관리, 평가 파이프라인, 비용 모니터링, 품질 대시보드 구성, 그리고 실전 선택 기준까지 코드 예제와 함께 다룹니다.- Published on
Ray Serve의 아키텍처, LLM 모델 서빙 배포, 오토스케일링, 멀티모델 패턴, KubeRay 운영을 실전 코드와 함께 총정리합니다.- Published on
오픈소스만으로 실시간 음성 챗봇을 구현하는 종합 가이드. Silero VAD, faster-whisper, Ollama, Piper TTS를 조합한 파이프라인에 barge-in(사용자 발화 시 즉시 응답 중단) 기능을 구현하는 상태머신 설계, Python 예시 코드, 지연시간 최적화, 한국어 품질 개선 팁까지 다룹니다.- Published on
NVIDIA Triton Inference Server를 활용한 GPU 모델 서빙 최적화 가이드. Dynamic Batching, Model Ensemble, TensorRT 통합, 멀티 모델 서빙, Kubernetes 배포, 성능 프로파일링과 프로덕션 트러블슈팅까지 다룹니다.- Published on
Weights & Biases(W&B)를 활용한 ML 실험 관리 실전 가이드. 실험 추적, Sweeps 하이퍼파라미터 튜닝, Artifacts 버전 관리, Model Registry, 팀 협업 기능까지 MLflow 비교와 함께 코드 예제로 다룹니다.- Published on
Feast Feature Store의 아키텍처와 오프라인/온라인 스토어 설계, 피처 정의와 엔티티 관리, 실시간 서빙 파이프라인 구축, 학습-서빙 스큐(Training-Serving Skew) 방지 전략, 그리고 프로덕션 운영 트러블슈팅까지 다루는 종합 가이드.- Published on
Forward Deployed Engineer(FDE)의 실제 역할, 일반 소프트웨어 엔지니어/솔루션 아키텍트와의 차이, 필요한 역량, 커리어 성장 경로, 90일 준비 로드맵을 최신 채용 공고와 업계 사례를 바탕으로 정리한다.- Published on
Kubeflow Pipelines v2의 아키텍처부터 KFP SDK로 ML 파이프라인 구축, 캐싱, 아티팩트 관리, CI/CD 통합, 프로덕션 운영 트러블슈팅까지.- Published on
Evidently AI와 MLflow를 활용한 ML 모델 프로덕션 모니터링 파이프라인 구축부터 데이터/컨셉 드리프트 탐지, 자동 재학습 트리거, 운영 트러블슈팅까지 다루는 종합 가이드.- Published on
MLflow 2.x의 실험 추적 설계부터 모델 레지스트리 운영, 아티팩트 관리, CI/CD 통합, 멀티테넌시, 프로덕션 배포까지 실전 가이드.- Published on
AI Platform Feature Store 동기화 설계 - 2026년 기준 실무 적용 가이드- Published on
AI 플랫폼: Feature Store와 RAGOps 블루프린트 2026 주제로 Why, How, When, 비교표, 트러블슈팅, 코드 예시, 퀴즈를 포함한 실전 가이드.- Published on
AI 플랫폼 스택 설계: Kubeflow, MLflow, KServe 통합 운영를 중심으로 Why/How/When, 비교표, 트러블슈팅, 실전 코드, 퀴즈까지 한 번에 정리한 실무형 문서입니다.- Published on
AI 플랫폼 모델 레지스트리와 A/B 배포 파이프라인 설계. MLflow Model Registry, 모델 버전 관리, 카나리/A/B 배포 전략, KServe InferenceService, 트래픽 분할, 롤백까지.- Published on
torchaudio로 오디오 로드, 스펙트로그램 변환, Mel 필터뱅크, MFCC, 음성인식(Wav2Vec2/Whisper), TTS, 화자 분리, 노이즈 제거까지. 오디오 AI의 모든 것을 PyTorch로 다룹니다.- Published on
torchvision의 transforms v2, 사전학습 모델(ResNet~ViT), 데이터셋, Object Detection(Faster R-CNN, YOLO), Segmentation, 그리고 실전 파인튜닝까지. 컴퓨터 비전 실무를 PyTorch로 정복합니다.- Published on
BentoML을 활용한 ML 모델 서빙을 실습합니다. 모델 패키징, API 구현, 멀티모델 파이프라인, Docker 빌드, Kubernetes 배포까지 핸즈온으로 다룹니다.- Published on
Kubeflow Pipelines v2의 KFP SDK를 사용하여 ML 파이프라인을 구축하는 실전 가이드. 컴포넌트 정의, 파이프라인 작성, 아티팩트 관리, Kubernetes 배포까지 코드 중심으로 다룹니다.- Published on
MLflow를 사용한 ML 실험 관리 전체 워크플로우를 실습합니다. Tracking으로 실험 기록, Model Registry로 버전 관리, 프로덕션 배포까지 핸즈온으로 구현합니다.- Published on
Ray Serve를 활용한 ML/LLM 모델 서빙의 핵심 개념부터 멀티모델 파이프라인, 오토스케일링, 배치 추론, 프로덕션 배포까지 코드 예제와 함께 다룹니다.- Published on
LangGraph로 상태 기반 AI 에이전트 워크플로우를 구축한다. StateGraph, 조건부 라우팅, 멀티에이전트 오케스트레이션, Human-in-the-Loop, 그리고 LangGraph Platform 배포까지 실전 코드 포함.