AWS + Unsloth: 4 Patterns Cutting Quantized LLM Memory 75%
AWS and Unsloth published four deployment patterns for quantized LLMs across EC2, SageMaker, EKS, and ECS that cut inference memory by roughly 75% and cost by up to 80%. The win comes from INT4/FP8 weights plus KV-cache and instance-class downshift, with Unsloth accelerating the fine-tune-to-GGUF pipeline at 2x speed and 70% less VRAM. We compare the four patterns, model the unit economics of a quantized 8B on g4dn vs g5 vs CPU, and include vLLM, Unsloth, Fargate, and EKS code. Treat the numbers as recent-trend guidance and validate on your own fleet.