LocalAIRun Blog

Practical deep dives, hands-on benchmarks, and editorial on running large language models locally. We cover every hardware tier — from Raspberry Pi clusters and 8 GB laptops to Mac Studio M3 Ultra and multi-GPU workstations — and the open-weight models that make local AI genuinely useful in 2026.

Unlike generic "best LLM" listicles that re-rank the same three models every month, our blog is where we publish the work behind the rankings: long-form cost analyses, real hardware teardowns, original benchmark runs, and decision frameworks for developers, creators, and homelab enthusiasts who want to own their AI stack.

What we cover

Cost analyses

Multi-year TCO comparisons: local hardware vs Claude, ChatGPT, Midjourney, Runway, and other API subscriptions. With electricity, RAM upgrades, and break-even math.

Hardware deep dives

Apple Silicon M-series vs NVIDIA RTX 40/50 vs AMD Strix Halo vs Snapdragon X Elite — what actually works for local inference, and what the marketing copy hides.

Model evaluations

Hands-on tests of Qwen3.5, Gemma 4, Llama 4 Scout, Phi-4-reasoning, DeepSeek-R1, Mistral Small 3.2, and gpt-oss on real workloads — not just MMLU scores.

Industry & policy

Hardware launches (NVIDIA RTX Spark, Snapdragon X, Apple M4), licensing changes (Llama 4 Community License, Gemma ToS), and what they mean for self-hosters.

Tooling & workflow

Ollama, LM Studio, vLLM, llama.cpp, MLX, Exo, Open WebUI — practical guides to set up production-grade local inference without surprises.

Use case studies

Code agents, RAG, image generation, voice, video — what works locally today and what still needs the cloud.

Latest posts

Stay updated

We publish one or two in-depth posts per month — no marketing, no "Top 10" roundup spam. For release-day news on local LLM launches, follow the project on GitHub or check the rankings page which is updated within hours of each major model release.