arXiv:2604.17709v2 Announce Type: replace Abstract: Existing works on large language model (LLM) decomposition mainly focus on improving performance on downstream tasks, but they ignore the poor parallel inference performance when trying to scale up the model size. To mitigate this important performance issue, this paper introduces DeInfer, a high-performance inference system dedicated to parallel inference of decomposed LLMs. It consists of multiple optimizations to maximize performance and be compatible with state-of-the-art optimization techniques. Extensive experiments are carried out to e
The increasing scale of LLMs highlights the urgent need for efficient inference solutions, driving innovation in parallel processing techniques to overcome computational bottlenecks.
This development improves the practical viability and cost-effectiveness of deploying large language models, making advanced AI more accessible and scalable across various applications.
The ability to perform more efficient parallel inference on decomposed LLMs will lower operational costs and broaden the types of applications that can effectively leverage large AI models.
- · AI developers
- · Cloud providers
- · Enterprises leveraging LLMs
- · Hardware manufacturers (GPUs)
- · Companies with inefficient LLM architectures
- · High-latency AI applications
Reduced computational costs for large language model deployment.
Accelerated development and adoption of more complex and integrated AI systems.
Potentially enables new classes of real-time AI applications previously uneconomical or impossible.
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