New Memory Technologies Emerge to Tackle AI Performance Bottlenecks

Key Intelligence

• ■Industry experts and new research identify memory, rather than raw computing power, as the primary bottleneck for advanced AI models.
• ■Integrated analog in-memory computing is emerging as a hardware innovation to process data directly within memory, improving efficiency.
• ■DeepSeek has introduced 'conditional memory' (Engram module) to decouple compute and RAM, efficiently store static knowledge, and reduce wasted GPU cycles in Large Language Models (LLMs).
• ■Storage solutions providers are developing high-capacity SSDs (e.g., 244TB) and challenging the efficacy of high-bandwidth flash as the sole solution for long-term AI scaling.
• ■These advancements aim to bypass limitations imposed by traditional GPU and High Bandwidth Memory (HBM) architectures, promising more efficient and scalable AI development.