How to Run an LLM Locally with LM Studio or Ollama
Choose one setup, run a small model on your own computer, and check whether its answer is useful.
AI
Local inference, AI models, prompt engineering and agent architecture.
How to Choose the Best Local LLM for Coding
Code completion, a multi-file bug fix and an explanation of legacy behavior are different tasks. Compare exact model variants on representative examples instead of choosing from a generic leaderboard.
What Is a Local LLM? Deployment, Privacy and Cost Explained
A local large language model (LLM) generates responses on hardware you operate rather than sending inference requests to a hosted model provider. That hardware can be your laptop, a workstation or a server on your organization's network.
Best Local LLM for Python Coding: Models, Setup and Tests
A local LLM for Python coding can help you write functions, explain unfamiliar modules, generate tests and debug errors without sending every prompt to a hosted model.
Local AI Server: A Complete Setup Guide for Beginners
A local AI server runs a model on hardware you control and makes it available to an application through an interface, usually an API. The machine can be your existing computer, a dedicated workstation or an internal server.
GPT-OSS-120B Hardware Requirements: Memory and Capacity Planning
OpenAI's model card describes operation within an 80 GB GPU, while its Ollama guide identifies at least 60 GB of VRAM or unified memory as a starting configuration.
GPT OSS: Hardware Requirements and Deployment Planning
A model that fits in memory may still miss the required response time, fail under concurrent requests or consume too much capacity at long context. This guide separates published model facts from the measurements needed to choose hardware.
What Is Prompt Engineering? A Practical Guide for Developers
The work is not finding a phrase that makes every answer correct. A prompt can clarify the task, but missing evidence, incorrect calculations and unauthorized actions require different controls.
DIY Local AI Server: Hardware Planning, Assembly Checks and Validation
Build a DIY local AI server around a workload and a verified component combination, not a list of large numbers. The model must fit its runtime, the GPU must fit the chassis and power supply, and the resulting system must meet a useful acceptance test.
Ollama Server Architecture Diagram: Request, Model and Application Boundaries
Ollama's server accepts an inference request, resolves the selected model and execution resources, and returns generated output. The surrounding application still owns user permissions, document access, tool execution and acceptance of the answer.
Opaque Recurrence in AI: Recurrent Depth, Hidden Reasoning and Evidence
“Opaque recurrence” is best unpacked into two separate questions: does a model reuse computation internally, and how much of its process is observable? Recurrent depth is an architectural mechanism. Hidden reasoning is an interface or visibility property. One does not establish the other.
OpenAI Agents API vs Agents SDK and Responses: An Enterprise Evaluation
The OpenAI Agents API is a managed agent-session interface. The Agents SDK is an application framework for building agent behavior in code.
Mac mini Local AI Server: Install, Test, Restart and Stop
An Apple Silicon Mac mini can host a local inference endpoint, but a working chat window is not the same as an unattended service. Verify the API, memory behavior, sleep policy, restart path and stop procedure before relying on it.
Local AI Server Architecture: A Bounded Team-Service Design
A local AI server architecture needs more than a model behind an HTTP endpoint. It must define who can use the service, which information each request may access, how inference capacity is shared and what happens when the result is wrong or the server is unavailable.