Akamai LKE

Agents That Own Their Inference is a workshop resource for building and operating production AI agents on dedicated GPUs. Its Jupyter notebooks, Kubernetes manifests, load-testing utilities, evaluation code, and Terraform setup guide participants through running a vLLM OpenAI-compatible endpoint and measuring inference behavior, including prefill and decode, time to first token, token generation time, KV-cache capacity, batching, queueing, and saturation. The workshop establishes a baseline, changes serving configurations, reruns realistic load tests, and evaluates output quality alongside throughput and latency. Exercises cover BF16-to-FP8 quantization, speculative decoding with a draft model, vLLM serving-policy changes, dense versus mixture-of-experts behavior, and deploying an agent on the measured inference endpoint. It can use an Akamai-hosted workshop namespace or a participant's Kubernetes cluster with an NVIDIA GPU; the repository is licensed under Apache-2.0.

vLLM is an open-source library and inference-serving engine for deploying large language models on GPUs and other hardware accelerators. Originally developed in UC Berkeley's Sky Computing Lab, it is maintained by a broad community and provides an OpenAI-compatible API server, as well as Anthropic Messages API and gRPC support. Its serving stack manages attention key-value memory with PagedAttention, continuous batching, chunked prefill, prefix caching, streaming generation, and disaggregated prefill, decode, and encode. It also supports speculative decoding, quantization, optimized attention and GEMM/MoE kernels, automatic kernel generation and graph-level transformations, structured outputs, tool calling, multiple decoding algorithms, and distributed inference through tensor, pipeline, data, expert, and context parallelism. The project integrates with Hugging Face models and supports decoder-only, mixture-of-experts, hybrid state-space, multimodal, embedding, retrieval, reward, and classification models. It can run across NVIDIA, AMD, Intel, and other supported accelerators, as well as x86, ARM, and PowerPC CPUs, and can be installed with uv or pip or built from source.

Akamai is a technology company whose developer resources cover inference optimization, AI agents, and managed Kubernetes. In the cited material, an Akamai developer advocate explains speculative decoding, in which a smaller draft model proposes tokens and a larger target model verifies them in one forward pass, and demonstrates the technique with vLLM.

An open GitHub guide and configuration project by jamesob for running state-of-the-art large language models locally. It documents a multi-GPU system using PCIe switches so GPUs can communicate peer-to-peer, including hardware selection, BIOS bifurcation, link-speed and ASPM settings, kernel and GRUB parameters, ACS configuration, and GPU power limiting. The repository also provides Docker-based serving configurations, a local speech-to-text configuration, and a GPU peer-to-peer bandwidth and latency benchmark.

EKS on HyperPod is an AWS infrastructure service for running Kubernetes-orchestrated H100 GPU clusters. DatologyAI used it as the execution environment for a large-scale synthetic-data pipeline combining Ray, KubeRay, and vLLM, with workflows spanning data curation, synthesis, training, and evaluation.