Llama.cpp vs. Ollama: Which is the Best Way to Run LLMs Offline? The era of "local AI" is officially here. With the release of powerful models like Llama 3, Mistral, and Phi-3, many developers and enthusiasts are looking to run Large Language Models (LLMs) on their own hardware. Why go offline? The reasons are simple: Privacy, zero latency from the cloud, no per-token costs, and the ability to experiment without restrictions. But once you decide to go local, you face the first big hurdle: How do you actually run these models? The two heavyweights in the scene are llama.cpp and Ollama . While they are related, they serve very different needs. In this post, we’ll break down the differences to help you decide which is right for your workflow. The Contenders: A Quick Overview Before diving into the comparison, let’s define what these tools actually are: llama.cpp: A high-performance C++ implementation of the Llama architecture (and many others). It is the "engine" tha...
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RAG vs Long Context for small organisations
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For small organizations , RAG (Retrieval-Augmented Generation) is often a better approach than relying solely on long-context LLMs because it delivers better accuracy, lower cost, and easier governance. Long Context Requires sending large amounts of data (hundreds of pages, sometimes millions of tokens) with every query. Token costs increase significantly as context size grows. Response latency also increases. Documents must be manually included in prompts. Applications need updates to ensure new content is supplied. RAG Only retrieves the most relevant documents or chunks. Sends a small subset of knowledge to the model. Reduces inference costs substantially. Add or update documents in the vector database/search index. New information becomes available immediately. For 100 docs — Long context is excellent — RAG is good For 10000 docs - Long context is good - RAG is excellent For 1million documents - Long context is impractical - RAG is Designed for this