On-Device SLM Inference: A 2026 Edge GPU Benchmark
A 2026 benchmark methodology for small language models on edge GPUs — latency, tokens/sec, memory, and cost for Phi, Gemma, and Qwen on Jetson-class hardware.
Edge MLOps Pipelines for Industrial IoT: 2026 Production Architecture
Production-grade edge MLOps pipeline for industrial IoT — training, packaging, OTA delivery, drift detection, and rollback under network constraints.
Time-Series Forecasting at the Edge: 2026 Production Patterns
Production patterns for time-series forecasting at the edge — Chronos-Bolt, TimesFM, TFT, quantization, and real-world latency budgets.
NVIDIA Jetson Thor: Humanoid Robot Compute Architecture
NVIDIA Jetson Thor architecture for humanoid robots — the compute stack, VLA model serving, real-time partitioning, power envelope, and where it fits.
Federated Learning for IoT: FedAvg, FedProx, and Privacy Architecture
Federated learning for IoT — FedAvg vs FedProx vs FedOpt aggregation, secure aggregation, differential privacy budgets, and a 2026 deployment blueprint for edge fleets.
LLMs on Jetson Orin Benchmarked (2026): tok/s & VRAM
Living benchmark — Llama 3.3 8B, Phi-4 14B, and Gemma 3 9B running on Jetson Orin AGX 64GB. Tokens/sec, time-to-first-token, memory, power. Updated quarterly.
Apple On-Device AI 2026: Neural Engine, Private Cloud Compute Architecture
How Apple Intelligence works — A19 Neural Engine, Private Cloud Compute, attested ML servers, model routing, and the privacy-preserving AI architecture.
TensorFlow Lite Micro on ESP32 (2026): Working Setup + Benchmarks
Step-by-step guide to running ML models on ESP32 using TensorFlow Lite Micro — quantization, memory budgeting, ESP-NN acceleration, and deployment patterns.
Edge AI Inference at Scale: NVIDIA Jetson, Intel, and Arm NPUs (Updated 2026)
Edge AI inference at scale, updated for 2026: NVIDIA Jetson Thor, Hailo and Arm Ethos NPUs, INT4/FP8 quantization, runtimes, and how to pick edge accelerators by TOPS-per-watt.