Building an AI-powered application requires more than adding a model to a traditional software project. Teams must manage code, prompts, data, models, and AI behavior as connected parts of the product. This practical framework combines: * Agile Scrum for short iterations, prioritized backlogs, and regular stakeholder feedback. * DevOps for continuous integration,
The simplest route is a full-stack Next.js application. It runs on Node.js while keeping the pages, authentication, API routes, and styling in one project. Codex works best when the prompt clearly states the outcome, constraints, acceptance criteria, and validation commands. That matches current official OpenAI prompting guidance, which
A strong way to use Codex for this is to make AGENTS.md define the architecture and engineering rules, then give Codex small issue-style tasks such as “build authentication” or “build the chat shell.” OpenAI recommends exactly this pattern: AGENTS.md supplies persistent repository context, while individual Codex prompts describe
The key is to think of vLLM GPU memory as a budget shared by several very different kinds of data. Some memory is essentially fixed when the model starts; some grows with request length and concurrency. For your vLLM workload, the mental model is: GPU VRAM │ ├── Model weights ← mostly fixed
A useful way to understand an LLM is to separate it into four pieces: architecture + weights + tokenizer + runtime configuration. An inference engine such as vLLM reads those pieces, constructs the neural network in GPU memory, loads the learned weights into it, allocates memory for inference, and then repeatedly runs the
1. Purpose This SOP describes how to use LiveBench to evaluate model quality through a production-style vLLM OpenAI-compatible API. Environment: ComponentConfigurationInference EnginevLLMAPIOpenAI-compatibleEndpointhttps://dev-va-vllm.eveon.comAuthenticationAPI Key / Bearer TokenBenchmarkLiveBenchInfrastructureAWS ALB → EC2 → Docker → vLLM LiveBench is primarily used to evaluate model quality, including reasoning, coding, mathematics, data analysis, language, and instruction following.
1. Purpose This SOP describes a secure and production-ready deployment of vLLM on Amazon Linux 2023, using an NVIDIA GPU-based EC2 G6 or newer instance and an AWS Application Load Balancer (ALB). The recommended architecture is: Client / Application | | HTTPS :443 v +-------------------------+ | AWS Application | | Load Balancer | | | | - TLS / ACM | | -
Version: 1.0 Platform: Amazon Linux 2023 Deployment Method: Docker (Recommended) Audience: System Administrators, DevOps Engineers, AI/ML Engineers 1. Purpose This document describes the recommended procedure for deploying vLLM on Amazon Linux 2023 using the official AWS Deep Learning Container (DLC). Using the AWS DLC is the simplest and
AWS EC2 GPU Instance Comparison FeatureAmazon EC2 G5Amazon EC2 G6Amazon EC2 G7Processor2nd-generation AMD EPYC processorsNewer-generation processors optimized for GPU workloadsCustom Intel Xeon 6 processorsGPUUp to 8 NVIDIA A10G Tensor Core GPUsNVIDIA GPUs with enhanced memory bandwidthUp to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUsGPU Memory24 GB per GPU24
Document Version: 1.0 Applies To: Ubuntu 22.04/24.04, Debian 12, RHEL/Rocky 9 (or equivalent Linux distributions) Model: google/gemma-4-12B-it (recommended) 1. Purpose This document describes the standard procedure for deploying a production-ready vLLM inference server running the Google Gemma 4 12B model using Docker Compose. The
1. Purpose This SOP describes how to install and configure Open WebUI on a Linux server using Docker Compose. The deployment supports: * Connection to Ollama running on the Linux host * Persistent storage for users, chats, configuration, and uploaded files * Optional Microsoft Entra ID authentication * Optional Google authentication * Optional NVIDIA GPU
1. Purpose This SOP provides instructions for installing and configuring Ollama on a Linux server, enabling remote access, verifying the service, and downloading an AI model. Recommended SOP Structure 1. Purpose 2. Scope 3. Prerequisites 4. Hardware Requirements 5. Operating System Requirements 6. GPU Verification 7. Install Ollama 8. Configure