Fatih E. Nar

Learn how to build an open cloud native architecture for AI agents. This blueprint explains how to improve workload isolation and implement inference routing.

Learn how to optimize deployment of vLLM for various traffic shapes, including high-concurrency chat, long-context RAG, high-throughput batch, and distributed AI-grid.

Learn about the three optimization levers for distributed AI inference: prefill/decode disaggregation, KV cache strategy, and speculative decoding.

Learn about the five-dimensional design space in modern LLM serving, including tensor, pipeline, expert, data, and context parallelism.

Learn about GPU compute kernels, their role in distributed AI inference, and the Hugging Face Kernel Hub.

Discover the advantages of vLLM, an open source inference server that speeds up generative AI applications by making better use of GPU memory.