Porting Large Language Model Inference Engine to Diverse AI Accelerators

The client faces difficulties in deploying high-performance large language models on on-premise hardware due to incompatible software frameworks and architectural differences between AI accelerators such as NVIDIA GPUs and Intel Gaudi chips. Existing solutions are optimized for specific platforms, limiting flexibility and efficiency when integrating new hardware accelerators into their AI infrastructure.

A mid to large-sized technology firm specializing in deploying AI solutions for enterprise clients, aiming to optimize AI model performance across varied hardware environments.

• Develop a portable inference engine capable of running large language models across multiple hardware platforms, including NVIDIA GPUs and Intel Gaudi accelerators.
• Ensure the inference process maintains high efficiency and predictive performance comparable to platform-specific implementations.
• Reduce time and cost associated with hardware-specific software rewrites for AI model deployment.
• Enhance the flexibility and scalability of on-premise AI solutions to support diverse hardware architectures.

• Support for porting and executing core tensor and matrix operations (kernels) originally designed for platform-specific frameworks (e.g., CUDA) to multiple hardware backends including SIMD-based architectures like Intel Gaudi.
• Automated detection and adjustment of kernel implementations to match specific hardware architecture constraints.
• Memory management modules that accommodate different memory transfer and structure requirements of target hardware.
• Workload distribution and parallel execution strategies tailored for SIMD and thread-based models to maximize hardware utilization and performance.

• Existing AI model repositories
• Hardware management and monitoring tools
• Memory transfer and data pipeline modules

• Support for scalable deployment across diverse on-premise hardware environments
• Achieve inference latency comparable to or better than platform-specific implementations
• Ensure data security and integrity during cross-platform memory and data transfers
• Maintain codebase flexibility for future hardware updates or additional accelerators

The project aims to enable seamless deployment of large language models on varied hardware accelerators, resulting in increased deployment flexibility and reduced development time. Expected outcomes include improved inference efficiency comparable to specialized solutions, accelerated hardware integration, and enhanced scalability of AI infrastructure, ultimately supporting faster time-to-market and cost savings in AI deployment processes.