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Published at October 21

On-Device LLMs for SMEs: Challenges and Opportunities

cs.AI

cs.SE

Released Date: October 21, 2024

Authors: Jeremy Stephen Gabriel Yee¹, Pai Chet Ng¹, Zhengkui Wang¹, Ian McLoughlin¹, Aik Beng Ng², Simon See²

Aff.: ¹Singapore Institute of Technology; ²NVIDIA AI Technology Center

Arxiv: https://arxiv.org/abs/2410.16070v2

Refer to caption

Feature	GPU (Graphics Processing Unit)	TPU (Tensor Processing Unit)
Design Focus	General-purpose parallel computing	Optimized specifically for tensor operations
Performance	High performance in a wide range of AI tasks	Exceptionally high performance in deep learning tasks
Efficiency	Good performance per watt, varying by model	Superior performance per watt, designed for energy efficiency
Flexibility	Supports a broad range of deep learning and general algorithms	Primarily focused on deep learning models
Ecosystem	Mature ecosystem with extensive support for various frameworks	Limited to specific frameworks optimized for TPUs
Cost	Consumer-grade GPUs are widely available and cost-effective	Generally more expensive and less accessible
Integration	Easier integration with existing systems and software	Requires specific software and infrastructure setup
Use Case	Ideal for SMEs with diverse computational needs	Best for SMEs focused heavily on deep learning deployments
Deployment	Flexible deployment in desktops, servers, and embedded systems	Typically used in cloud environments or specialized setups