# What is Local LLM Runtime? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Infrastructure  
> **Canonical Citation:** [https://spidits.com/ai-glossary/local-llm-runtime](https://spidits.com/ai-glossary/local-llm-runtime)

## Definition
A Local LLM Runtime is an execution engine (such as Ollama, llama.cpp, or LM Studio) engineered to run quantized open-weights language models locally on consumer hardware without sending data to cloud APIs.

## Architectural Mechanics & Deep Dive
A Local LLM Runtime is an execution framework optimized for running open-weights language models directly on local hardware architectures without relying on remote cloud APIs. Built on lightweight runtimes like llama.cpp and Ollama, these engines utilize GGUF and AWQ quantization formats to maximize memory bandwidth utilization across Apple Silicon Unified Memory and consumer GPUs.

## Industry Applications & Real-World Use Cases
Privacy-focused AI assistants, offline code completion, and edge device intelligence

## Frequently Asked Questions

### What model format is used by local LLM runtimes?
Local runtimes primarily use quantized GGUF format files optimized for CPU, Apple Silicon Metal, and consumer GPU execution.

### What are the hardware requirements for running a local LLM?
Requirements depend on model size: a 7B model quantized to 4-bit (Q4_K_M) requires ~6GB of Unified Memory or GPU VRAM.

## Related Terminology
- [llm](https://spidits.com/ai-glossary/llm)
- [rag](https://spidits.com/ai-glossary/rag)
- [vector-database](https://spidits.com/ai-glossary/vector-database)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*