Cut AI inference costs: Quantization and sparsity guide

Deploying large language models in production poses challenges: high memory needs, latency, and escalating costs. As AI workloads scale, inefficient inference serving limits throughput and strains budgets.

This e-book covers inference optimization via model compression and runtime improvements. Learn how these strategies cut resource use while preserving accuracy. Topics include:

· Quantization and sparsity to reduce model size from 140GB to 40GB with minimal accuracy loss
· Runtime optimizations like paged attention and batching for better GPU use
· Full-stack strategies for models and infrastructure

Download the e-book to optimize AI inference across hybrid clouds.