Reproduce “Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models” on OpenResearch

About this paper

Abhimanyu Bambhaniya, Ritik Raj, Geonhwa Jeong, Souvik Kundu, Sudarshan Srinivasan, Suvinay Subramanian, Midhilesh Elavazhagan, Madhu Kumar and 1 more

GenZ estimates how workloads, models, inference stages, and platforms interact, helping designers compare configurations before hardware deployment. It models prompt processing separately from token-by-token generation, which reuses a growing key–value cache. For LLaMA 3 8B, BF16, batch size 16, it reports 5.82% geomean error against benchmark results across three architectures, validating selected comparisons rather than every workload or hypothetical platform.

Key insights

• Inference optimizations, such as chunked prefill, reveal varying bottlenecks (memory-bound vs. compute-bound) across different LLM architectures, and speculative decoding increases memory requirements while its performance gains are highly dependent on acceptance probability.
• Different LLM architectures exhibit distinct hardware scaling characteristics, with Mamba models offering context-independent runtime for the decode stage, while memory capacity emerges as a critical and potentially unsustainable bottleneck for future extreme-scale LLM applications.
• Optimal hardware characteristic scaling is highly dependent on the inference stage, with TFLOPS primarily benefiting compute-bound prefill, memory bandwidth being crucial for memory-bound decode, and interconnect characteristics significantly impacting collective communication in both stages.

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