The author examines the trade-offs between running large language models via Ollama versus llama.cpp's WebUI. While Ollama is celebrated for its user-friendly interface and effortless model management, it introduces a small performance overhead by acting as an abstraction layer over the inference engine. In contrast, llama.cpp provides faster generation speeds and highly granular control over hardware offloading and sampling parameters. Although llama.cpp requires more manual effort to launch and switch models, it is preferred for users seeking maximum performance and technical customization.
* Performance comparison of local LLM inference
* Comparison of user experience versus technical control
* Evaluation of abstraction overhead in AI tools
* Performance comparison of local LLM inference
* Comparison of user experience versus technical control
* Evaluation of abstraction overhead in AI tools
Running large language models locally often runs into hardware limitations that prevent complex problem-solving. This article explains a hybrid approach where a local model acts as a junior engineer for routine tasks but escalates difficult issues to cloud-based models like Claude when it gets stuck. This orchestration system allows for a privacy-focused, local-first workflow without sacrificing the high-level reasoning power of massive commercial AI.
- Ollama for local inference and model management
- LiteLLM as a routing layer to provide a unified API for both local and cloud models
- OpenRouter or Anthropic's API for flexible cloud escalation
- A simple orchestration system to manage retries and task handovers
- Ollama for local inference and model management
- LiteLLM as a routing layer to provide a unified API for both local and cloud models
- OpenRouter or Anthropic's API for flexible cloud escalation
- A simple orchestration system to manage retries and task handovers
This article details 7 lessons the author learned while self-hosting Large Language Models (LLMs), covering topics like the importance of memory bandwidth, quantization, electricity costs, hardware choices beyond Nvidia, prompt engineering, Mixture of Experts models, and starting with simpler tools like LM Studio.
