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edge-inference

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Mis à jour1 juin 2026 à 09:15

Edge / on-device AI inference architecture: running models on MCUs, NPUs, mobile, and mini-PCs; quantization for edge, TOPS/memory/energy budgets, TinyML, and the latency case for on-device vs cloud. Architect-level. USE WHEN: designing on-device/edge AI, "edge inference", "on-device", "NPU", "TinyML", "quantization", "Jetson", "Coral", "Hailo", local LLM on small hardware, offline/low-latency inference, energy-constrained ML. DO NOT USE FOR: cloud GPU serving (use `inference-serving-topology`); choosing datacenter accelerators (use `ai-hardware-selection`); RAG app code (use rag skills).

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Ingénieurs en matériel informatiqueArchitecture et ingénierie·SOC 17-2061

SKILL.md

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name	edge-inference
description	Edge / on-device AI inference architecture: running models on MCUs, NPUs, mobile, and mini-PCs; quantization for edge, TOPS/memory/energy budgets, TinyML, and the latency case for on-device vs cloud. Architect-level. USE WHEN: designing on-device/edge AI, "edge inference", "on-device", "NPU", "TinyML", "quantization", "Jetson", "Coral", "Hailo", local LLM on small hardware, offline/low-latency inference, energy-constrained ML. DO NOT USE FOR: cloud GPU serving (use `inference-serving-topology`); choosing datacenter accelerators (use `ai-hardware-selection`); RAG app code (use rag skills).
allowed-tools	Read, Grep, Glob

Edge / On-Device Inference

Why on-device (the decision drivers)

Latency: on-device token/inference in ~tens of ms vs 200–500ms cloud round-trip — decisive for voice, AR, control loops.
Privacy / offline / cost: data never leaves the device; works without connectivity; no per-call cloud cost.
Cost of doing it: tight memory/energy/thermal budgets; smaller models; more engineering.

The hardware tiers (match model to silicon)

Tier	Silicon	Typical model
MCU / TinyML	Cortex-M + tiny NPU (sub-$1 class)	KB-MB models: keyword spotting, anomaly detection (TFLite Micro)
Mobile / AI-PC	Phone NPU, laptop NPU (tens of TOPS)	Quantized 3–8B LLMs, vision
Edge box	Jetson Orin/Thor, Coral, Hailo (40+ TOPS)	7–13B LLMs, multi-camera vision

Architectural levers

Quantization is the key enabler: FP16 → INT8 → INT4 trades accuracy for memory/throughput/energy. Most edge LLMs run INT4/INT8. Validate accuracy loss.
Model choice: small instruct/distilled models (e.g. 3–8B class) and vision models sized to the NPU's memory bandwidth, not just TOPS.
Runtime: TFLite/LiteRT, ONNX Runtime, ExecuTorch, llama.cpp/Ollama, vendor SDKs (TensorRT for Jetson). Pick what targets the accelerator.
Budgets: state the TOPS, RAM, and energy-per-inference budget up front — they bound everything. Batch=1, KV-cache memory dominates LLM RAM.

When to recommend edge vs cloud vs hybrid

Hard latency / offline / privacy / per-unit cost → on-device.
Large model / variable load / centralized updates → cloud serving.
Both needs → hybrid (small local model + cloud escalation): see hybrid-edge-cloud.

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name	edge-inference
description	Edge / on-device AI inference architecture: running models on MCUs, NPUs, mobile, and mini-PCs; quantization for edge, TOPS/memory/energy budgets, TinyML, and the latency case for on-device vs cloud. Architect-level. USE WHEN: designing on-device/edge AI, "edge inference", "on-device", "NPU", "TinyML", "quantization", "Jetson", "Coral", "Hailo", local LLM on small hardware, offline/low-latency inference, energy-constrained ML. DO NOT USE FOR: cloud GPU serving (use `inference-serving-topology`); choosing datacenter accelerators (use `ai-hardware-selection`); RAG app code (use rag skills).
allowed-tools	Read, Grep, Glob

Edge / On-Device Inference

Why on-device (the decision drivers)

Latency: on-device token/inference in ~tens of ms vs 200–500ms cloud round-trip — decisive for voice, AR, control loops.
Privacy / offline / cost: data never leaves the device; works without connectivity; no per-call cloud cost.
Cost of doing it: tight memory/energy/thermal budgets; smaller models; more engineering.

The hardware tiers (match model to silicon)

Tier	Silicon	Typical model
MCU / TinyML	Cortex-M + tiny NPU (sub-$1 class)	KB-MB models: keyword spotting, anomaly detection (TFLite Micro)
Mobile / AI-PC	Phone NPU, laptop NPU (tens of TOPS)	Quantized 3–8B LLMs, vision
Edge box	Jetson Orin/Thor, Coral, Hailo (40+ TOPS)	7–13B LLMs, multi-camera vision

Architectural levers

Quantization is the key enabler: FP16 → INT8 → INT4 trades accuracy for memory/throughput/energy. Most edge LLMs run INT4/INT8. Validate accuracy loss.
Model choice: small instruct/distilled models (e.g. 3–8B class) and vision models sized to the NPU's memory bandwidth, not just TOPS.
Runtime: TFLite/LiteRT, ONNX Runtime, ExecuTorch, llama.cpp/Ollama, vendor SDKs (TensorRT for Jetson). Pick what targets the accelerator.
Budgets: state the TOPS, RAM, and energy-per-inference budget up front — they bound everything. Batch=1, KV-cache memory dominates LLM RAM.

When to recommend edge vs cloud vs hybrid

Hard latency / offline / privacy / per-unit cost → on-device.
Large model / variable load / centralized updates → cloud serving.
Both needs → hybrid (small local model + cloud escalation): see hybrid-edge-cloud.