VQV Signal
LLM serving systems face a trade-off between latency and throughput due to distinct prefill and decode phases in inference. Techniques like tensor parallelism, pipeline parallelism, and hybrid configurations help optimize GPU resource use.
LLM serving systems face a trade-off between latency and throughput due to distinct prefill and decode phases in inference. Techniques like tensor parallelism, pipeline parallelism, and hybrid configurations help optimize GPU resource use.
AI-assisted summary based on the listed source.
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...
Efficiently managing the trade-off between time-to-first-token and inter-token latency is crucial for improving LLM inference performance. Understanding parallelism strategies enables better utilization of distributed GPUs in large-scale deployments.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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