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1.4 The Accuracy Cost of Quantization

On the Edge: Agentic AI for Neural Proc... Foundations of NPU-Optimized Agents

Chapter 1.2 laid out the quantization recipes Intel NPU supports: INT8-sym, INT4-sym group-128 or channel-wise, NF4 on Lunar Lake, FP8 on Panther Lake. The hardware story ended there. This section is the missing other half — what those recipes actually cost yo...

1.5 Speculative Decoding

On the Edge: Agentic AI for Neural Proc... Foundations of NPU-Optimized Agents

Chapter 1.3 established the bandwidth ceiling as the binding constraint on LLM decode: 136.5 GB/s shared LPDDR5X, ~25 GB/s effective NPU quota, ~6–20 tok/s sustained throughput for 3B–8B INT4 models. The natural follow-up question is whether there's any way ar...

3.4 Structured Outputs and Constrained Decoding

On the Edge: Agentic AI for Neural Proc... Tool Use & Integration Patterns

An agent is only as reliable as the parser that reads its output. Chapter 3.1 covered designing the tools; Chapter 3.2 weighed local against cloud; Chapter 3.3 routed work across devices on the SoC. This section closes the loop on the agent-tool contract: how ...

4.4 Security and Privacy on the Edge

On the Edge: Agentic AI for Neural Proc... Production Deployment & Observability

"It runs on the device, so it's private" is the marketing line. It's also a half-truth that has caused real production incidents. Chapter 4.1 through 4.3 covered the deployment, observability, and rollout machinery; this section is about the threat model that ...