A collection of 10 posts
A hardware-first comparison of Gemma 4 and Llama 4 for local deployment in 2026. Includes full VRAM tables, benchmark data, licensing analysis, and a use-case decision matrix to help you pick the right model for your machine.
Last updated April 2026 — refreshed for current model/tool versions and the post-launch year of evidence. One year after Meta shipped Llama 4 Scout and Maverick on April 5, 2025, the verdict is in. This is a sober retrospective on what went wrong, what the benchmarks actually said once the
The artificial intelligence space continues to evolve rapidly, and two of the most powerful contenders in 2025 are Meta’s Llama 4 and OpenAI’s GPT-4.5. Each model brings unique capabilities and innovations, catering to a broad spectrum of use cases—from enterprise automation to creative content generation. This
In the rapidly evolving landscape of artificial intelligence, two giants have unveiled their latest large language models (LLMs), each pushing the boundaries of natural language understanding, reasoning, and multimodal capabilities. Meta’s Llama 4 and Google’s Gemini 2.5 Pro represent major leaps forward, delivering new features and performance
When Meta shipped Llama 4 and Anthropic shipped Claude 3.7 Sonnet within six weeks of each other in early 2025, the pairing looked like a clean fight: a giant open-weight, natively-multimodal Mixture-of-Experts model versus the first hybrid-reasoning frontier model you could only reach through an API. More than a
Meta's Llama 4 and Mistral AI's Mistral 7B sit at two very different points on the open-model spectrum. Llama 4 is a large, Mixture-of-Experts family built for long context, multimodal input, and frontier-level reasoning. Mistral 7B is a compact, dense model built to run fast and
Running LLama 4 locally on Ubuntu provides an exceptional opportunity to harness advanced artificial intelligence while keeping your data secure and lowering operational costs. This guide walks you through every step—from setting up your system and installing necessary software to fine-tuning your model and troubleshooting common issues. Want the
Last updated April 2026 — refreshed for current model/tool versions. Meta's Llama 4 family (Scout, Maverick, and the still-internal Behemoth) is a Mixture-of-Experts (MoE) lineup with 17B active parameters and a native 10M-token context on Scout. Running it on Windows in 2026 is no longer exotic, but it
Meta AI's LLaMA (Large Language Model Meta AI) represents a breakthrough in local AI processing. With the introduction of LLaMA 4, Windows users can now run advanced AI models on their own machines without relying solely on cloud services. This guide walks you through everything you need to
Quick answer. Llama 4 Scout (109B MoE / 17B active) runs on Apple Silicon Macs with 32 GB+ unified memory via Ollama or MLX. Maverick (400B / 17B active) needs 96 GB+ for Q4_K_M. M3/M4 Max with MLX beats llama.cpp Metal by 20-40% tok/s at the same