Crushing the Cost Curve: Efficient AI Inference at Scale

As AI demands continue to grow, companies are forced to rethink chip design and optimize AI inference workflows to reduce costs and improve efficiency

TL;DR
• Companies are rethinking chip design to reduce costs and improve efficiency in AI inference.
• Metrum AI and Solidigm collaborate to optimize AI inference workflows.
• Offloading workloads to storage can drive efficiency in AI inference.

Summary
The increasing demands of artificial intelligence are prompting companies to rethink their approach to chip design and AI inference at scale. Organizations are focusing on reducing the total cost of ownership and improving system efficiency. Metrum AI Inc. and Solidigm are collaborating on a solution that offloads data from expensive DRAM to high-performance storage, leveraging NAND storage to house massive datasets. This approach allows enterprises to run large AI models on legacy hardware by batching workloads, reducing costs without sacrificing performance. The solution involves sophisticated software engineering, including the use of the DiskANN algorithm to optimize retrieval. Key areas of focus include optimizing the key-value cache, often referred to as the operating system of the AI factory, and offloading parts of neural network layers onto solid-state drives for GPU-constrained models. The goal is to deliver accurate products that save time and drive top-line revenue results while minimizing costs. Main strategies include:
• Offloading data from DRAM to high-performance storage
• Leveraging NAND storage for massive datasets
• Optimizing the key-value cache
• Offloading parts of neural network layers onto solid-state drives The industry is shifting towards optimizing AI inference at scale, with a focus on cost-efficient solutions and system-level thinking. Companies are looking to reduce their reliance on expensive DRAM and instead utilize high-density storage to lower costs. By optimizing storage and retrieval processes, organizations can run large AI models more efficiently and effectively. The collaboration between Metrum AI and Solidigm showcases the potential for innovative solutions in this space, enabling enterprises to achieve accurate and efficient AI inference at scale.

As the industry continues to evolve, it's essential to prioritize efficiency and cost-effectiveness in AI inference workflows to drive business outcomes.