How to Run and Orchestrate LLMs Locally

Open-weight large language models can run locally using model weight files and inference engines, offering potential advantages in cost control, privacy, offline operation, experimentation, and continuous workloads, while requiring sufficient fast memory and hardware. Model files contain billions of learned parameters organized as tensors, and quantization reduces their memory footprint, with 4-bit versions commonly balancing local performance and quality. Hardware capacity determines whether a model and its context cache fit in memory, while memory bandwidth drives generation speed; discrete-GPU VRAM generally offers higher speed, whereas unified-memory systems can accommodate larger models. Tools such as Ollama provide model management and local REST APIs, LM Studio combines downloads, inference, and chat in a desktop application, and Open WebUI supplies a browser interface for compatible model servers. The write-up demonstrates using the self-hosted Kestra orchestration platform to schedule a local GitHub-issue summarization workflow, pause a hosted-model request for human cost approval, and compare three local models in parallel. The benchmark illustrates that parallel execution can be slower when models exceed available memory and must be swapped, as sequential runs on a 24 GB MacBook completed faster than simultaneous ones.

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