hardware limitations

Why on-device agentic AI can't keep up

2 months ago

Local on-device AI offers privacy, zero latency, and no API costs, but is largely impractical for most consumer devices.
Consumer hardware faces RAM constraints, with 8-16GB common, leaving only 4-8GB for AI models after OS and apps.
A capable 7B parameter model requires ~5GB, but agentic tasks also need large KV cache, quickly exceeding 8GB RAM.
Even small agentic tasks need at least 32K token context, requiring ~16GB RAM just for AI on devices.
Rising RAM prices and competition with datacenter HBM worsen memory availability for consumer devices.
On-device inference speed drops drastically with context size, e.g., from 100 to <10 tok/s at 16K context on a desktop GPU.
Battery and heat issues make sustained on-device agentic AI use impractical on phones.
Cloud inference avoids these limits but would require enormous datacenter capacity for billions of devices.
Realistic agentic AI on consumer devices remains years away without major hardware or architecture breakthroughs.