Web Development

A data-driven article comparing the leading local models of 2026. Focuses on practical developer metrics rather than abstract scores. Key Sections: 1. **Methodology:** Hardware used, prompt set (coding, reasoning, creative). 2. **The Contenders:** MiniMax2.5, Llama 3, Mistral Large 2, Gemma 2. 3. **Results – Coding:** Python/JS generation accuracy. 4. **Results – Speed:** Tokens per second […]

A guide for DevOps engineers on orchestrating LLMs availability and scaling using Kubernetes. Key Sections: 1. **Prerequisites:** GPU Operator setup, Nvidia Container Toolkit. 2. **Serving Options:** KServe vs Ray Serve vs simple Deployment. 3. **Resource Management:** Requests/Limits for GPU, dealing with bin-packing. 4. **Scaling:** HPA based on custom metrics (queue depth). 5. **Example:** Full Helm

A guide for CTOs and DevSecOps engineers on hardening local AI deployments. Just because it’s local doesn’t mean it’s secure. Key Sections: 1. **Threat Vectors:** Prompt injection, model theft, training data poisoning. 2. **Network Security:** Air-gapping requirements, mTLS for inference usage. 3. **Access Control:** Implementing API keys and usage quotas for internal LLM APIs. 4.

A code-heavy tutorial on building a ‘Chat with your PDF’ app that never touches the internet. Uses widely available open-source tools. Key Sections: 1. **Architecture:** Ingestion -> Embedding -> Vector Store -> Retrieval -> Generation. 2. **The Stack:** LangChain, Ollama (Llama 3), ChromaDB or pgvector, Nomad/local embeddings. 3. **Code Implementation:** Python implementation steps. Handling document

A hardware-focused tutorial on building a dedicated AI inference server using consumer components. Focus on the sweet spot of dual used RTX 3090s or a single RTX 4090. Key Sections: 1. **Component Selection:** Why VRAM is king. The concept of ‘VRAM per dollar’. 2. **The Build:** Physical assembly notes, cooling requirements for continuous load. 3.

A comparative guide for developers seeking a private, free alternative to GitHub Copilot. Contrasts the polished experience of Cursor with the DIY flexibility of VS Code + Continue. Key Sections: 1. **The Privacy Imperative:** Why send code to the cloud if you don’t have to? 2. **Setup Guide:** Configuring Ollama with DeepSeek-Coder-V2. 3. **Integration:** Setting

A technical migration guide for teams outgrowing Ollama’s developer-friendly experience and needing vLLM’s production throughput. Key Sections: 1. **When to Migrate:** Identifying bottlenecks (concurrency, latency spikes). 2. **Architecture Comparison:** Ollama’s monolithic approach vs vLLM’s PagedAttention and decoupled architecture. 3. **Migration Steps:** Converting Modelfiles to Docker-compose setups, handling quantization format changes (GGUF to AWQ/GPTQ). 4. **API

A comprehensive pillar guide on architecting, deploying, and managing local Large Language Models (LLMs) for enterprise and production use cases in 2026. This article must move beyond ‘how to install Ollama’ and cover the full stack: hardware selection (H100 vs A100 vs RTX 4090 clusters), inference engine selection (vLLM vs TGI vs TensorRT-LLM), and observability

Build your own private Copilot alternative that runs entirely locally. Zero subscription fees, complete privacy, and surprisingly good code completion. Continue reading Local AI Coding Assistant: Complete VS Code + Ollama + Continue Setup on SitePoint.

Ollama is perfect for local development, but when your team grows past 3 concurrent users, performance drops dramatically. This guide shows you exactly when to migrate to vLLM and how to do it without downtime. Continue reading From Ollama to vLLM: A Migration Guide for Growing Teams on SitePoint.