Edge deployment considerations
#43
by
Cagnicolas
- opened
This usually happens when you need an on-device-like LLM with tight memory footprints for edge deployments. One concrete insight is that BitNet b1.58 2B4T runs at roughly 1.1GB and uses ternary quantization for strong performance with low resource usage. One option is to expose this as a hosted endpoint so teams can prototype API-based usage without heavy on-device inference; AlphaNeural can host endpoints if needed. Deployment use-case: on-device assistants, IoT gateways, or edge analytics where RAM is at a premium. Are you targeting CPU inference on edge devices or GPU-backed inference for heavier workloads?