LLM Inference
LLM inference is the process of executing trained language models to generate text predictions and responses. Unlike cloud-based API services, local inference involves running these models directly on individual machines or edge devices. This approach has become increasingly practical with the development of optimized inference engines and quantization techniques that reduce computational requirements while maintaining output quality.
Local Deployment and Tools
Llama.cpp is a widely-adopted inference engine that enables efficient [[concepts/native-support|local model exec
Recent Advancements: Speculative Decoding
Recent developments have focused on accelerating inference through enhanced speculative decoding techniques:
- DeepSeek DSpark]]]]: A speed layer for LLMs introduced by DeepSeek that significantly accelerates inference without compromising quality.
- Demonstrated ability to double the speed of models like Qwen3.
- Acts as an optimization layer leveraging enhanced speculative decoding]] mechanisms.
- See detailed analysis in DeepSeek DSpark: LLM Inference Acceleration via Enhanced Speculative Decoding.