Many LLMs exceed single GPU capacity, necessitating partitioning across distributed GPUs. The vLLM effectively manages this through a centralized KV cache manager for optimal performance.
The vLLM implementation supports Megatron-LM style model parallelism, which requires the GPUs to synchronize intermediate results while efficiently handling distributed memory across multiple processes.
By sharing a KV cache manager among GPU workers, vLLM ensures that multiple processes can utilize the same cache efficiently, thus optimizing resource management and execution time.
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