AWS Enables Multi-LoRA Inference for Mixture of Experts Models in vLLM

According to an Amazon AWS blog post, the company has implemented multi-LoRA (Low-Rank Adaptation) inference capabilities for Mixture of Experts (MoE) models in vLLM, enabling efficient serving of dozens of fine-tuned models simultaneously on Amazon SageMaker AI and Amazon Bedrock.

The post explains AWS’s implementation approach for multi-LoRA inference specifically for MoE models and describes kernel-level optimizations performed to improve performance. The company uses GPT-OSS 20B as the primary example model throughout their explanation.

This development addresses a key challenge in production AI deployments: efficiently serving multiple specialized versions of large language models without proportionally scaling infrastructure costs. By leveraging LoRA adapters—which store only the fine-tuned differences rather than full model copies—organizations can switch between dozens of task-specific models while maintaining a single base model in memory.

The implementation is available on both Amazon SageMaker AI, AWS’s machine learning platform, and Amazon Bedrock, their managed foundation model service. According to the source, the solution enables organizations to benefit from these optimizations for their own multi-model serving use cases.