Developers recognize the critical need for efficient AI solutions across diverse computing environments. As enterprises race to deploy AI projects, developers can gain a competitive edge by leveraging the AI and machine learning (ML) tools in software development kits (SDKs) to optimize large language models (LLMs) that power chatbots, virtual assistants, and other AI systems. Hardware-specific SDKs are designed to enable seamless integration with on-device hardware, enhancing model execution and accelerating neural network inference to improve the model’s ability to apply patterns to new input.
With the transition to AI PCs, developers face important hardware-optimization choices for performance and efficiency. For example, they can build AI applications on devices powered by Intel® Core™ Ultra processors with hybrid architectures—using both Performance-cores (P-cores) and Efficient-cores (E-cores)—or on devices powered by Qualcomm® Snapdragon® Arm64 systems on a chip (SoCs), which are often used for mobile devices. In this study, commissioned by Intel, Prowess Consulting tested the Intel® OpenVINO™ toolkit and the Qualcomm® AI Engine Direct SDK on Dell™ XPS™ 13 AI PCs to determine the better choice for developers. The Intel OpenVINO toolkit earned higher scores in target hardware support, platform compatibility, features, ease of use, and other factors.
TL;DR
Intel® OpenVINO™ toolkit is the stronger choice for deploying large language models (LLMs) on AI PCs. In a head-to-head comparison commissioned by Intel and conducted by Prowess Consulting, the Intel Distribution of OpenVINO toolkit scored higher than Qualcomm® AI Engine Direct SDK across key criteria including hardware support, platform compatibility, model conversion, and inference performance. Tested on Dell™ XPS™ 13 devices, the Intel Distribution of OpenVINO toolkit proved easier to use and more versatile for building chatbot pipelines with Meta Llama 3.2-3B. Its broader ecosystem and support for hybrid CPU/NPU architectures make it ideal for secure, local AI deployment.
Evidence: see “Which Toolkit Provides the Best Optimization for Large Language Models?” and Table 1 in the source.
FAQ
Q: Which toolkit performed better for LLM deployment?
A: Intel® OpenVINO™ outperformed Qualcomm® AI Engine Direct SDK in hardware support, platform compatibility, and inference. See Table 1 in the source.
Q: What hardware platforms were used in testing?
A: The study tested Dell™ XPS™ 13 AI PCs powered by Intel® Core™ Ultra processors and Qualcomm® Snapdragon® Arm64 SoCs. See “Study Parameters.”
Q: Why is local LLM deployment beneficial?
A: Local deployment reduces latency, improves data privacy, and avoids reliance on cloud APIs. See “Executive Summary.”
Q: What model was used to evaluate chatbot performance?
A: The Meta Llama 3.2-3B model was used to test inference and pipeline optimization. See “Which Tools Are Best for Working with LLMs?”
Q: Do both toolkits support quantization?
A: Yes. Both Intel® OpenVINO™ and Qualcomm® SDK support INT8 quantization workflows for optimized inference. See “INT8 Quantization Workflow.”
Q: What are the practical implications for developers?
A: Developers using OpenVINO benefit from easier integration, broader hardware support, and a more mature ecosystem for building AI pipelines. See “Toolkit Comparison.”
Explore more research from Prowess Consulting: https://prowessconsulting.com/resources