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Part 6 of “vLLM in 2026: An Infrastructure Engineer’s Field Guide” For years, training and inference were separate worlds with separate clusters and separate teams. That boundary is dissolving. Reinforcement learning turns your inference engine into part of the training loop. Speculative decoding needs draft models that must be trained against the target you serve.

Part 5 of “vLLM in 2026: An Infrastructure Engineer’s Field Guide” Quantization is the closest thing inference has to a free lunch, and it isn’t free. Cutting precision shrinks weights, shrinks the KV cache, and speeds up memory-bound decode. It also risks quality regressions that don’t show up until a user finds them. This post

Part 4 of “vLLM in 2026: An Infrastructure Engineer’s Field Guide” Capacity planning for chat was relatively simple: users send a prompt, the model streams an answer, and you size for concurrent conversations. Agents change the traffic shape completely. A single user request can fan out into dozens of model calls, each with a growing

Part 3 of “vLLM in 2026: An Infrastructure Engineer’s Field Guide” If you’ve designed a lossless RoCEv2 fabric or debugged NCCL rings, you already know most of what matters here. Serving a frontier-scale model is no longer “one model on one GPU.” It is a distributed system whose performance is set by collectives, KV cache

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.

Learn how retina scan security works, the privacy risks of biometric authentication, template protection, anti-spoofing controls, and biometric data protection.

Learn secure data destruction methods based on NIST SP 800-88 Rev. 2, including data sanitization, cryptographic erase, SSD sanitization, degaussing, and physical destruction.

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