How Full-Stack Codesign Aims to Solve AI's Memory Shortage by Designing Hardware and Software Together

At a glance

As AI grows and handles longer conversations, GPU high-speed memory is running short. Instead of simply adding more memory, NVIDIA says it designs GPUs, CPUs, networks, storage, and software together so the same memory can do more work.

Key points

1. 1

   AI model parameters (the values a model learned during training) and the KV cache (temporary space that holds conversation context) take up a lot of high-speed memory.

2. 2

   Simply increasing high-speed memory capacity can cost more money and power, because data movement speed cannot keep up.

3. 3

   So frequently used data is kept in fast memory, while less urgent data is placed in larger, cheaper storage tiers.

4. 4

   NVIDIA claims this design raised its GPU memory capacity by only about 3% but increased throughput per dollar by up to 30 times.

Why it matters

As AI services grow, memory costs and power problems grow too, so methods that do more work with the same resources are important.