Business Challenge
Situation
A global enterprise aimed to evaluate the performance of endpoint devices for deploying Generative AI workloads, focusing on advanced use cases like Retrieval-Augmented Generation (RAG). The objective was to assess the feasibility of utilizing state-of-the-art hardware, such as Intel NPU-enabled laptops, to support enterprise AI applications.
Challenges
Performance Limitations
- Ensuring reliable AI processing for computationally intensive workloads like querying large language models (LLMs).
- Addressing latency and throughput issues for real-time responses.
Hardware Compatibility
- Adapting the AI application to various hardware architectures, including Meteor Lake, Alder Lake-U, and legacy laptops.
Optimization Needs
- Balancing performance and accuracy through techniques like model quantization.
Autonomous Operation
- Developing a secure, air-gapped solution where all AI processing occurs locally on the endpoint device.
Solution
To address these challenges, a state-of-the-art LLM-based RAG-driven chatbot application was developed:
Technical Design
- Built using the Llama3 model with 7B parameters, optimized by quantizing to int4 for efficient processing.
- The chatbot supported both text-based and audio-based interactions, delivering accurate and contextually relevant responses.
Deployment Tested
- across Meteor Lake-based AI PCs, Alder Lake-U devices, and legacy laptops to evaluate performance across hardware types.
- Key performance metrics like inference rate, memory usage, and CPU/GPU/NPU utilization were monitored.
Secure Processing
- All AI computations were performed locally, ensuring compliance with stringent security and privacy requirements.
Optimization Techniques
- Leveraged quantization and hardware-specific optimizations to enhance the AI application's performance while minimizing resource utilization.
Technology & Tools
Hardware:
Intel Meteor Lake (32GB, Intel Core Ultra 7 155U), Alder Lake-U (64GB, Intel Core i7), and legacy Intel Core i5 laptops.
Framework:
Intel OpenVINO for model training, hosting, and querying.
Model:
Llama3 with 7B parameters, quantized to int4 for optimized performance.
Workload:
RAG-driven chatbot trained on domain-specific datasets.
Business Outcomes
Performance Insights
- Meteor Lake-based AI PCs demonstrated superior performance in inference rates and reduced latency compared to legacy devices, highlighting their potential for enterprise AI workloads.
Scalability
- Optimized LLM deployment on endpoint devices showcased the potential for scaling
- Generative AI applications without heavy infrastructure dependencies.
Security
- The air-gapped deployment ensured data privacy and compliance, a critical requirement for enterprise use cases.
Actionable Recommendations
- Insights were provided to enhance performance further, including porting the application to additional hardware stacks (e.g., Apple, Qualcomm, AMD) and expanding support for sophisticated AI workloads.