Inference Engineering demystifies AI serving’s stack

This fills a major gap in AI education: practical inference knowledge remains scattered across vendor documentation, papers, and infrastructure blogs.

• –Connects low-level hardware concepts with production concerns like autoscaling, observability, and reliability
• –Covers techniques including quantization, speculative decoding, KV-cache reuse, parallelism, and disaggregation
• –Gives newcomers a structured path into a rapidly growing infrastructure discipline
• –Its NVIDIA and datacenter focus makes it especially relevant for cloud deployment, but less comprehensive for edge inference
• –The free digital edition lowers the barrier for developers exploring AI systems beyond model APIs