aiqbench analysis

Netflix details its hybrid inference architecture combining Triton for scheduling with vLLM for execution, addressing critical production challenges like version compatibility and custom decoding requirements. This case study offers practical insights into managing diverse model sizes across CPU and GPU workloads within a JVM-based serving layer without disrupting existing workflows. The analysis highlights the operational trade-offs between tightly coupled Python backends and more independent deployment strategies in large-scale LLM serving environments.