Large language model optimization refers to techniques that reduce computational costs and improve inference speed while maintaining model accuracy—typically achieving 40-70% memory reduction through methods like quantization, pruning, and knowledge distillation.

As LLMs like GPT-4 and Llama models exceed 100 billion parameters, optimization has become critical. According to Stanford’s 2025 AI Index Report, inference costs for enterprise LLM deployments average $0.03-$0.12 per 1,000 tokens, making optimization essential for profitability.

What Are the Most Effective Quantization Methods?

INT8 quantization reduces model size by 75% with minimal accuracy loss. Meta’s research shows their 4-bit quantization on Llama 3 70B maintains 96% of original performance while cutting memory from 140GB to 35GB. GPTQ and AWQ are leading post-training quantization frameworks in production.

How Does Retrieval-Augmented Generation Improve Efficiency?

RAG reduces hallucinations by 60% while allowing smaller models to compete with larger ones. Companies like Perplexity use RAG with 7B parameter models instead of 70B+ models, cutting infrastructure costs by 80%. The key is high-quality vector databases—Pinecone and Weaviate lead enterprise adoption.

Which Pruning Techniques Deliver Real Results?

Structured pruning removes entire attention heads or layers. Google DeepMind’s 2025 paper demonstrated 30% parameter reduction in PaLM 2 with only 2% accuracy drop. Magnitude-based pruning is easiest to implement, while lottery ticket hypothesis methods show promise for extreme compression.