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为什么 DeepSeek 大规模部署很便宜,本地很贵
AI前线· 2025-07-04 06:10
Core Insights - The article discusses the trade-off between throughput and latency in AI inference services, particularly focusing on models like DeepSeek-V3, which are said to be fast and cheap at scale but slow and expensive when run locally [1][12]. - It highlights the importance of batch processing in improving GPU efficiency, where larger batch sizes can lead to higher throughput but increased latency due to waiting for the batch to fill [2][12]. Batch Processing and GPU Efficiency - Batch processing allows multiple tokens to be processed simultaneously, leveraging the GPU's ability to perform large matrix multiplications efficiently [3][4]. - The efficiency of GPUs is maximized when executing large matrix multiplications in a single command, reducing overhead and memory access times compared to multiple smaller operations [4][12]. - In inference servers, a "collect window" is used to queue user requests, balancing the need for low latency (5-10 milliseconds) against the benefits of higher throughput with larger batch sizes [5][12]. Expert Mixture Models and Pipeline Efficiency - Expert mixture models, like DeepSeek-V3, require larger batch sizes to maintain GPU efficiency, as they involve multiple independent weight blocks that can lead to low throughput if not properly batched [6][12]. - Large models with many layers need to avoid "pipeline bubbles" by ensuring that the batch size exceeds the number of layers in the pipeline, which can otherwise lead to inefficiencies and increased latency [8][12]. - The article notes that maintaining a full queue is challenging due to the need for sequential processing of tokens, which complicates the batching of requests from the same user [9][10]. Implications for Inference Providers - Inference providers must choose batch sizes that optimize throughput while managing latency, as larger batch sizes can lead to significant delays for users waiting for their tokens to be processed [12]. - The performance of models from companies like OpenAI and Anthropic suggests they may utilize more efficient architectures or advanced inference techniques to achieve faster response times compared to models like DeepSeek [12].