Scaling language models upward is becoming impractical due to economic, energetic, and practical limitations. Instead, model compression and distillation offer advancements in AI by making models faster, lighter, cheaper, and deployable. Techniques like knowledge distillation, quantization, pruning, and low-rank adaptation enhance model efficiency, enabling real-world applications such as on-device intelligence and low-latency responses.
Table of contents
Why LLM Compression and Distillation Is the FutureThe Scaling Era Is Slowing DownWhat Is Model Compression?2. Quantization3. Pruning4. Low-Rank Adaptation & PEFTLLMs Need to Leave the Cloud272 Impressions