What is the trade-off between inference latency and quality-of-service (e.g., response accuracy) when deployin

Abstract

Abstract: The rapid scaling of large language models (LLMs) has unveiled critical limitations in current hardware architectures, including constraints in memory capacity, computational efficiency, and interconnection bandwidth. DeepSeek-V3, trained on 2,048 NVIDIA H800 GPUs, demonstrates how hardware-aware model co-design can effectively address these challenges, enabling cost-efficient training and inference at scale. This paper presents an in-depth analysis of the DeepSeek-V3/R1 model architecture and its AI infrastructure, highlighting key innovations such as Multi-head Latent Attention (MLA) for enh

Research Question

What is the trade-off between inference latency and quality-of-service (e.g., response accuracy) when deploying Llama, Mistral, Qwen, and DeepSeek under varying hardware constraints (e.g., GPU vs. CPU inference)?

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