APreQEL: Adaptive Mixed Precision Quantization For Edge LLMs
arXiv:2603.23575v1 Announce Type: new Abstract: Today, large language models have demonstrated their strengths in various tasks ranging from reasoning, code generation, and complex problem solving. However, this advancement comes with a high computational cost and memory requirements, making it challenging to deploy these models on edge devices to ensure real-time responses and data privacy. Quantization is one common approach to reducing memory use, but most methods apply it uniformly across all layers. This does not account for the fact that different layers may respond differently to reduced precision. Importantly, memory consumption and computational throughput are not necessarily aligned, further complicating deployment decisions. This paper proposes an adaptive mixed precision quantization mechanism that balances memory, latency, and accuracy in edge deployment under user-defined priorities. This is achieved by analyzing the layer-wise contribution and by inferring how different
arXiv:2603.23575v1 Announce Type: new Abstract: Today, large language models have demonstrated their strengths in various tasks ranging from reasoning, code generation, and complex problem solving. However, this advancement comes with a high computational cost and memory requirements, making it challenging to deploy these models on edge devices to ensure real-time responses and data privacy. Quantization is one common approach to reducing memory use, but most methods apply it uniformly across all layers. This does not account for the fact that different layers may respond differently to reduced precision. Importantly, memory consumption and computational throughput are not necessarily aligned, further complicating deployment decisions. This paper proposes an adaptive mixed precision quantization mechanism that balances memory, latency, and accuracy in edge deployment under user-defined priorities. This is achieved by analyzing the layer-wise contribution and by inferring how different quantization types behave across the target hardware platform in order to assign the most suitable quantization type to each layer. This integration ensures that layer importance and the overall performance trade-offs are jointly respected in this design. Our work unlocks new configuration designs that uniform quantization cannot achieve, expanding the solution space to efficiently deploy the LLMs on resource-constrained devices.
Executive Summary
This article proposes APreQEL, an adaptive mixed precision quantization mechanism for efficient deployment of large language models (LLMs) on edge devices. Unlike uniform quantization, APreQEL analyzes layer-wise contributions and infers optimal quantization types based on hardware platforms, balancing memory, latency, and accuracy. This approach unlocks new configuration designs, expanding the solution space for deploying LLMs on resource-constrained devices. The proposed mechanism ensures layer importance and performance trade-offs are jointly respected, enabling efficient deployment of LLMs while preserving data privacy and real-time responsiveness.
Key Points
- ▸ APreQEL is an adaptive mixed precision quantization mechanism for efficient deployment of LLMs on edge devices.
- ▸ APreQEL analyzes layer-wise contributions and infers optimal quantization types based on hardware platforms.
- ▸ APreQEL balances memory, latency, and accuracy, ensuring layer importance and performance trade-offs are jointly respected.
Merits
Scalability
APreQEL's adaptive nature allows it to be applied to various LLM architectures and hardware platforms, making it a highly scalable solution.
Flexibility
APreQEL's ability to balance memory, latency, and accuracy enables users to define priorities and optimize deployment configurations accordingly.
Demerits
Implementation Complexity
APreQEL's adaptive mechanism may introduce additional complexity in implementation, requiring significant computational resources and expertise.
Quantization Type Selection
The selection of optimal quantization types may be challenging, especially for complex hardware platforms, potentially leading to suboptimal performance.
Expert Commentary
The proposed APreQEL mechanism demonstrates a significant advancement in the field of model quantization and deployment. By analyzing layer-wise contributions and inferring optimal quantization types, APreQEL achieves a delicate balance between memory, latency, and accuracy. However, the complexity of implementation and quantization type selection may pose challenges. Nevertheless, the scalability and flexibility of APreQEL make it an attractive solution for various LLM architectures and hardware platforms.
Recommendations
- ✓ Future research should focus on developing more efficient and robust implementation methods for APreQEL, reducing the complexity of quantization type selection.
- ✓ APreQEL should be evaluated on a wider range of LLM architectures and hardware platforms to demonstrate its adaptability and scalability.
Sources
Original: arXiv - cs.LG
