Memory

 

I assume personal computing (desktop, laptop) will contine to be an important component in human lives. The question is this: will chatbots drive a decline in the need for "edge" memory (SSD, HDD) or will the need for such memory actually increase?

AI Overview

Chatbots will likely increase, not decrease, the need for "edge" memory (local SSDs and HDDs) in personal computing

. While most of a large language model's (LLM) training is performed in the cloud, running these models locally for real-time inference and fine-tuning has significant advantages for users. 

The shift toward local AI will increase storage demand

1. Privacy and control: When a chatbot runs entirely on your device, your data never leaves your immediate environment. Sensitive information, such as financial records or personal conversations, is processed and stored securely on your hardware. This eliminates the risk of it being transmitted to and kept on third-party servers. 

2. Faster performance: Local processing eliminates the latency associated with communicating with cloud-based servers. For tasks that require immediate, real-time responses—such as voice control or advanced, in-app functionality—having the AI model on your local machine is far more efficient. 

3. Offline functionality: A local AI model continues to function even without an internet connection. This is crucial for users in remote locations or those who experience network outages, ensuring uninterrupted access to AI features. 

4. Data ownership: Running a local AI empowers you, the user, to control what your AI remembers and for how long. The future of AI memory is heading toward user-curated memory, where you can explicitly approve or delete information the AI retains. 

5. Personalization: A local AI assistant can develop a deeper, more private understanding of your work patterns and preferences without sharing this information with external servers. This leads to a more personalized and effective experience that compounds over time. 

6. Edge AI data processing: Increasingly, AI is being deployed at the "edge" (on the user's device) to handle real-time data from IoT devices, wearable tech, and other sensors. This processing requires fast, reliable local storage, with SSDs playing a crucial role. 

Local AI requires significant storage

The core models that power chatbots are substantial in size. As of early 2025, even with advanced compression techniques, large language models can consume many gigabytes of storage. The trend is toward larger, more capable models, which will likely increase this footprint over time. 

For example, hardware provider Puget Systems recommends high-capacity NVMe SSDs (2-8TB) for users running LLMs on personal servers, specifically citing the large storage needs of model parameters and datasets. Manufacturers are already developing specialized high-performance SSDs to meet the demands of these AI workloads. 

The overall trend shows a future where the data generated and used by AI is stored across a hybrid system:

• Local, high-speed storage (SSD): For real-time inference, user context, and frequently accessed data needed by the AI.
• Centralized cloud storage: For massive-scale AI model training and vast, less frequently accessed datasets.
• High-capacity, low-cost storage (HDD): For long-term archiving of training data and other large data volumes.