Scaling LLM Inference via Parallelism: Throughput-Latency Trade-off
Large Language Model (LLM) serving systems must balance latency and throughput while efficiently utilizing distributed GPU resources. This challenge arises because inference consists of two fundamentally different phases: prefill, which determines time-to-first-token (TTFT), and decode, which determines inter-token latency (ITL). The use of tensor parallelism (TP), pipeline parallelism (PP), and hybrid configurations exposes different trade-offs between these latency dimensions, making the optimal parallelism configuration highly dependent on workload characteristics, latency requirements, and context length. Moreover, despite the widespread adoption of distributed inference, there is sti...
| Authors | Javad Mirzaei, Jeebak Mitra |
|---|---|
| Primary category | cs.DC |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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