As an AI Infrastructure Engineer.
Please learn:
- GPU/VRAM fundamentals, quantization & batching
- vLLM / TensorRT-LLM / inference optimization
- KV caching, speculative decoding & token throughput
- Distributed training basics (DDP/FSDP/DeepSpeed)
- Model serving & autoscaling
- Vector DB retrieval pipelines
- Prompt caching & cost optimization
- Observability for LLM apps
This is what production AI teams actually care about.