Author: Abhishek Kunal

AI inference explained: Explore prefill, decode, KV caching, batching, GPU optimization, and scalable LLM serving for enterprise AI workloads.

Explore 5 hands-on LLM Kubernetes projects covering vLLM serving, DevOps, GPU autoscaling, KServe, and KEDA scale-to-zero inference.

Explore vLLM RL rollouts, speculative decoding, draft model training, and inference optimization to build efficient enterprise AI infrastructure.

Explore LLM quantization with vLLM. Compare PTQ vs QAT, FP8, INT4, KV cache compression, and GPU memory optimization for efficient AI inference.

Explore agentic AI capacity planning with vLLM. Learn how prefix caching, KV offloading, and GPU optimization improve inference efficiency.

Explore distributed LLM inference, GPU networking, vLLM, expert parallelism, KV cache transfers, and Kubernetes-native AI serving with llm-d.

Compare vLLM multi-hardware inference across NVIDIA, AMD, Google TPU and Intel for performance, portability, cost and enterprise AI infrastructure. Today, vLLM multi-hardware inference can run across NVIDIA GPUs, AMD Instinct accelerators, Google TPUs, and Intel hardware

Learn how vLLM handles LLM inference using PagedAttention, KV cache, continuous batching, chunked prefill, torch.compile and production monitoring.

Build a sovereign LLM inference platform with Kubernetes, NVIDIA H200 GPUs, vLLM, NIM, LiteLLM and F5 for secure enterprise AI infrastructure.

Build practical AI infrastructure skills across 12 hands-on stages covering Kubernetes, NVIDIA GPUs, LLM inference, autoscaling, observability, security and more.