Understanding and Optimizing Multi-Stage AI Inference Pipelines
New simulator tackles multi-stage LLM workflows like RAG and reasoning that break traditional benchmarks.
A research team from Georgia Tech and Google has published a new paper introducing MIST (Heterogeneous Multi-stage LLM inference Execution Simulator), a tool designed to model the increasingly complex reality of modern AI inference. Today's LLM serving extends far beyond simple text generation, involving multi-stage pipelines that combine Retrieval-Augmented Generation (RAG), dynamic model routing, multi-step reasoning, and key-value (KV) cache retrieval. These stages have wildly different computational demands, requiring distributed systems that mix GPUs, specialized ASICs, CPUs, and memory-centric architectures. Existing simulators lack the fidelity to model these heterogeneous workflows, creating a critical gap for architects.
MIST addresses this by supporting heterogeneous clients that can execute multiple models concurrently across complex hardware hierarchies. It integrates real hardware traces with analytical modeling to capture critical trade-offs like memory bandwidth contention, inter-cluster communication latency, and batching efficiency in hybrid CPU-accelerator deployments. The paper uses case studies to explore the impact of reasoning stages on end-to-end latency and optimal batching strategies for hybrid pipelines. For the first time, system designers have a tool to navigate architectural decisions—such as the implications of remote KV cache retrieval—providing actionable insights for optimizing the full stack of next-generation AI workloads, from software pipelines to hardware selection.
- MIST simulates multi-stage LLM pipelines including RAG, reasoning, and KV cache retrieval across CPUs, GPUs, and ASICs.
- It models real-world bottlenecks like memory bandwidth contention and inter-cluster latency, which prior simulators ignored.
- The tool helps architects optimize hardware-software co-design for complex AI workloads, moving beyond simple prefill-decode benchmarks.
Why It Matters
Enables efficient design of AI infrastructure for real-world applications like agents and RAG, moving beyond lab benchmarks to production performance.