Best Tools for Running LLMs Locally in 2026
Compare the best tools for running large language models locally: Ollama, LM Studio, llama.cpp, GPT4All, and LocalAI. Includes hardware requirements and model recommendations.
James Kowalski
AI Benchmarks & Tools Analyst
James is a software engineer turned tech writer who spent six years building backend systems at a fintech startup in Chicago before pivoting to full-time analysis of AI tools and infrastructure. His engineering background means he doesn't just read the spec sheet - he runs the benchmarks, profiles the latency, and checks whether the marketing claims hold up under real workloads.
He studied Computer Science at the University of Illinois at Urbana-Champaign, where he first got hooked on natural language processing during a senior research project on sentiment analysis. He later completed a certificate in data journalism from Northwestern's Medill School.
At Awesome Agents, James owns the leaderboards and tool comparison coverage. He maintains the site's benchmark tracking methodology and is the person who actually runs the numbers before publishing any ranking. He is also an open-source advocate and contributes to several projects in the LLM inference space.
Based in Chicago, IL.
Articles by James Kowalski
DeepSeek V3.2 Review: GPT-5 Performance at a Fraction of the Cost
A thorough review of DeepSeek V3.2, the 671B parameter MoE model that delivers frontier-level performance at dramatically lower cost with an MIT license.
How to Run an Open-Source LLM on Your Own Computer
A practical tutorial on running open-source language models locally using Ollama, llama.cpp, and LM Studio, with hardware requirements and model recommendations.
Claude Code Review: Terminal-First AI Coding at Its Finest
A hands-on review of Anthropic's Claude Code CLI, a terminal-first AI coding assistant that excels at large refactors, architecture work, and complex multi-file projects.
Best AI-Powered Search Engines in 2026
Compare the best AI-powered search engines of 2026: Perplexity AI, Google AI Overviews, Bing Copilot, You.com, Phind, and Kagi. How AI search differs from traditional search.
Building Your First AI Agent: A Step-by-Step Introduction
A beginner-friendly guide to building your first AI agent with Python, covering core concepts like LLMs, tools, and memory, with a practical example using LangChain.
MCP: The Universal Plug-and-Play Standard for AI Tools
Everything you need to know about the Model Context Protocol (MCP): what it does, why it matters, which frameworks support it, and how to use it with real-world examples.
Claude Opus 4.5
Anthropic's November 2025 flagship model delivers top SWE-bench scores, a new effort parameter for reasoning control, and a 66% price cut from its predecessor.
Claude Haiku 4.5
Anthropic's fastest and most cost-efficient model, delivering 73.3% on SWE-bench Verified and first-in-family extended thinking and computer use at $1/$5 per million tokens.
GPT-OSS 20B
OpenAI's open-weight 21B MoE reasoning model with 131K context, Apache 2.0 license, and o3-mini-level benchmark performance running in 16 GB of memory.
Gemini 2.5 Pro
Google DeepMind's flagship thinking model with 1M-token context, 84% GPQA Diamond, and native multimodal understanding of text, images, audio, and video.
OpenAI o3-pro
OpenAI's maximum-compute reasoning model targets the hardest problems where o3 falls short, at $20/$80 per million tokens.
