How Much VRAM & Hardware You Need for Local LLMs
Not sure if your hardware can run a local LLM? This guide covers VRAM requirements by model, RAM minimums, GPU tiers, and how to run without a dedicated GPU.
Gemma vs CodeLlama: Which Is Better for Coding?
Gemma vs CodeLlama for coding: compare coding quality, VRAM, speed, and license side by side. Includes a decision matrix to help you pick the right model for your workflow.
Can You Run a Local LLM Without a GPU? Yes — Here’s How
Yes — you can run a local LLM without a GPU. Covers CPU-only inference, best quantised models (GGUF/Q4), and step-by-step setup with llama.cpp and Ollama in CPU mode.
Best Local LLM for Coding: Top Models Ranked 2026
The best local LLMs for coding, ranked by coding strength, VRAM, and speed. Compare CodeLlama, Gemma & Mistral and choose the right model for your setup.
Why AI Agents Fail in Production: A Developer’s Perspective
When an agent fails locally, you can usually see it instantly in your terminal output. In production, failures are often silent, expensive, and unpredictable. The non-deterministic nature of LLMs mean
How to Run Local LLMs with Ollama for Developers
Ollama has revolutionized local inference by abstracting away the complex Python environments and CUDA driver nightmare that traditionally plagued local AI setup.
Building Internal Automation Tools with Local LLMs
For years, developers have defaulted to cloud-based APIs for AI features. However, when building tooling meant exclusively for internal use—such as log analyzers, internal documentation assistants, or
AI Agent Memory Systems: How to Manage State for LLMs
[IMAGE: Diagram showing short term vs long term agent memory flow]
AI Agent Architecture Patterns: A Blueprint for Developers
Just as microservices and event-driven architectures standardized how we build web applications, specific design patterns are emerging for autonomous agents. This guide provides a technical blueprint
How to Run an LLM Locally on Existing Hardware
Step-by-step guide to running an LLM locally on existing hardware. Install Ollama or llama.cpp, pull a model, and connect a UI — no new hardware required.