Inference Optimization

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Techniques for making LLM inference faster, cheaper, and more memory-efficient in production.

Key Techniques

• Quantization — reduce weight precision (INT8, INT4) with minimal quality loss
• KV-Cache — cache key/value matrices across tokens to avoid recomputation
• Speculative Decoding — use small model to draft, large model to verify
• vLLM — efficient inference engine with PagedAttention for memory management
• Continuous Batching — process multiple requests efficiently

Related

• Model Serving (production deployment)
• Mixed Precision Training (related precision techniques)

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nlp llm inference optimization