AI BriefingCost-Efficient LLM Serving Using GPU Tensor Parallelism on Amazon EC2 G5/G6 Instances
Key point
This introduces a method for reducing large-scale LLM serving costs by leveraging GPU Tensor Parallelism on Amazon EC2 G5/G6 instances.
Details
Deploying state-of-the-art LLMs such as Llama 3 70B, Qwen 72B, and EXAONE 3.5 32B requires large amounts of GPU memory, ranging from 40GB up to as much as 150GB. It is common to use Amazon P5 instances equipped with H100/H200 for this purpose, but this leads to a heavy cost burden.
By leveraging GPU Tensor Parallelism on Amazon EC2 G5/G6 instances, models can be served in a more economical environment. This enables building a strategy to efficiently run large-scale models without the need for expensive instances.
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