AI/ML—6 min read—December 20, 2024
Local LLM Optimization Techniques
Practical strategies for running LLMs efficiently on local hardware.
Running LLMs locally changes how systems are designed. Instead of relying on external APIs, you control latency, cost, and execution patterns. This guide focuses on practical optimization techniques for local LLM setups like LM Studio and Ollama.
01
Model Selection
Choosing the right model size and architecture is critical for balancing performance and resource usage.
02
Reducing LLM Calls
Optimizing workflows to reduce unnecessary LLM calls often provides more gains than hardware-level optimization.
03
Memory Optimization
Techniques like quantization and efficient caching reduce memory footprint while maintaining performance.
04
Inference Optimization
Batching, efficient tokenization, and optimized kernels help reduce latency.
/// Summary
Local LLM optimization is not just about hardware — it’s about designing efficient workflows and reducing unnecessary computation.