Skip to content
IoT Digital Twin PLM
  • Home
  • About
  • Blog
  • Consult
  • Contact
  • Cookie Policy
  • Disclaimer
  • Privacy Policy
  • Terms of Service

Edge AI

  • Home
  • Blog
  • Edge AI
  • Page 2
Yocto on Jetson vs Ubuntu L4T: Building Production Edge AI Images with OE4T

Yocto on Jetson vs Ubuntu L4T: Building Production Edge AI Images with OE4T

Posted by By MPRAUTO MPRAUTO September 20, 2026Posted inTech1 Comment
NVIDIA now officially supports Yocto on Jetson via OE4T. How a Yocto image compares with Ubuntu L4T on footprint, reproducibility and attack surface.
Read More
Which Small Language Models Actually Run on CPU in 2026: 11 Models Compared

Which Small Language Models Actually Run on CPU in 2026: 11 Models Compared

Posted by By MPRAUTO MPRAUTO September 19, 2026Posted inTech1 Comment
Eleven small language models compared for CPU-only inference in 2026: real Q4 file sizes, official GGUF availability, licences and the memory-bandwidth ceiling.
Read More
ExecuTorch 1.5 On-Device LLM Serving (2026): Batched Scheduling, Cancellation and Off-Graph KV Cache

ExecuTorch 1.5 On-Device LLM Serving (2026): Batched Scheduling, Cancellation and Off-Graph KV Cache

Posted by By MPRAUTO MPRAUTO September 19, 2026Posted inTechNo Comments
ExecuTorch 1.5 shipped multi-method export, batched request scheduling, bounded cancellation and off-graph KV cache. What it changes for on-device LLM apps.
Read More
Jetson Thor vs Jetson Orin AGX: 2026 Edge AI Upgrade Guide

Jetson Thor vs Jetson Orin AGX: 2026 Edge AI Upgrade Guide

Posted by By MPRAUTO MPRAUTO September 17, 2026Posted inAI2 Comments
NVIDIA Jetson Thor vs Jetson Orin AGX in 2026: TOPS, GPU architecture, memory bandwidth, power, price and whether the Thor upgrade is worth it for edge AI.
Read More
INT4 vs INT8 vs FP8 on Edge NPUs: The 2026 Quantization Trade-off

INT4 vs INT8 vs FP8 on Edge NPUs: The 2026 Quantization Trade-off

Posted by By MPRAUTO MPRAUTO August 13, 2026Posted inAINo Comments
INT4, INT8, and FP8 quantization compared on edge NPUs in 2026: accuracy loss, latency, and memory trade-offs for vision and LLM workloads.
Read More
Hailo-10H vs Jetson Orin Nano (2026): Same CV Workload Tested

Hailo-10H vs Jetson Orin Nano (2026): Same CV Workload Tested

Posted by By MPRAUTO MPRAUTO August 6, 2026Posted inTech1 Comment
Hailo-10H vs Jetson Orin Nano head-to-head on the same computer-vision workload: TOPS/W, latency, framework support, memory and price. A 2026 edge-inference pick.
Read More
On-Device LLM Runtimes (2026): llama.cpp vs MLC vs ONNX

On-Device LLM Runtimes (2026): llama.cpp vs MLC vs ONNX

Posted by By MPRAUTO MPRAUTO August 4, 2026Posted inTech1 Comment
On-device LLM runtimes compared: llama.cpp vs MLC-LLM vs ONNX Runtime on edge SoCs - backends, quantization, throughput, memory and portability. 2026 decision guide.
Read More
Jetson Thor vs Hailo-10H vs Coral (2026): Edge Inference Pick

Jetson Thor vs Hailo-10H vs Coral (2026): Edge Inference Pick

Posted by By MPRAUTO MPRAUTO August 4, 2026Posted inTech1 Comment
Jetson Thor vs Hailo-10H vs Google Coral for edge AI inference: TOPS/W, framework support, memory, price and the workload each wins. 2026 hardware decision guide.
Read More
Small Language Models on Device: Edge Inference Architecture (2026)

Small Language Models on Device: Edge Inference Architecture (2026)

Posted by By MPRAUTO MPRAUTO July 10, 2026Posted inAI1 Comment
How to run small language models (SLMs) on-device: model sizing, distillation, quantization, NPU acceleration, memory budgets, and when a 1-8B SLM beats a cloud LLM.
Read More
NVIDIA Jetson + K3s: Edge AI Cluster Tutorial (2026)

NVIDIA Jetson + K3s: Edge AI Cluster Tutorial (2026)

Posted by By mprcba June 28, 2026Posted inKubernetesNo Comments
A hands-on NVIDIA Jetson and K3s edge AI cluster tutorial: provision nodes, enable GPU scheduling, deploy a vision model, and run inference at the edge.
Read More

Posts pagination

Previous page 1 2 3 4 Next page
  • Claude Opus 5.5: Anthropic’s New Flagship, Benchmarked
  • MCP Goes Stateless: Migrating to the 2026-07-28 Spec
  • DuckDB v2.0 vs 1.5.x: Benchmarks for IIoT Telemetry
  • Karmada Graduates: A Multi-Cluster K8s ADR
  • Jetson T3000 vs T5000: JetPack 7.2.1 Compared
  • Digit 5 Safety Architecture: Reference Design for 2026
  • ISO 23247-5 Digital Thread Reference Architecture 2026
  • langchain-mcp-adapters vs Native langchain.mcp (2026)
  • Delta Lake 4.4 vs 4.3: The Spark 4.2 Upgrade Trap
  • KEDA 2.21 vs 2.20: CVE Fix & Breaking Scaler Changes
  • MoveIt Pro 10.0 vs 9.4: The Breaking Upgrade Guide
  • Isaac ROS 5.0 vs 4.6: NITROS Is Gone, Now What?
  • Ignition 8.1 vs 8.3: 2026 Migration Guide Update
  • Aras Innovator R40 vs R38: .NET 10 Migration Guide
  • SGLang 0.5.18 vs 0.5.15: What Changed and How to Upgrade
  • Helm 4.3 vs Helm 3.22: Migrating Before Helm 3 EOL
  • Milvus 3.0 vs 2.6: Lake-Native Vector Search Upgrade Guide 2026
  • MoveIt 2 vs MoveIt Pro 2026: What Qualcomm’s PickNik Deal Means
  • ONNX Runtime 1.30 vs 1.29: What Changed for Edge AI in 2026
  • JetPack 7.2.1 vs 6.2.2 on Jetson Orin: Migration Guide 2026
  • EMQX 6.3 LTS vs 5.8 LTS: Breaking Changes and Migration
  • CODESYS 4 vs CODESYS 3: What the 1.0 Web IDE Changes
  • vLLM 0.28 to 0.30 Migration: Model Runner V2 Default, Breaking Changes
  • Apache Spark 4.2 vs 4.1: CDC, Geospatial and Arrow-by-Default Risks
  • Terraform 1.16 vs OpenTofu 1.13: Where the IaC Forks Now Diverge
  • LeRobot v0.6 vs v0.5: What Changed and How to Migrate
  • OpenVINO 2026.4 vs 2025.4: What Changed for Edge LLMs and NPUs
  • Jetson Orin Nano 2 vs Orin Nano Super: 2x Inference, Same Socket
  • OPC UA 1.03 vs 1.05: Certification Ends 2026, Migration Guide
  • OpenPLC Runtime v4 vs v3: What Changed and How to Migrate (2026)
  • ClickHouse 26.8 LTS vs 26.3: Pipelined SQL, Iceberg Writes and Upgrade Risk
  • World Action Models vs VLAs: Cosmos 3, VLA-JEPA and FastWAM Compared
  • Cilium 1.20 ExternalAuth vs oauth2-proxy vs Istio AuthorizationPolicy
  • OPC UA FX v1.00.04 vs v1.00.03: What Changed in Part 81 and Part 84
  • What a ChatGPT Query Actually Costs in Energy and Water: Every Number, Traced to Source
  • MLPerf Edge Agentic Inference: How TensorRT Edge-LLM Beat llama.cpp 6.4x
  • Kubernetes 1.37 Gang Scheduling vs Volcano, Kueue and YuniKorn
  • Isaac Lab 3.0 vs 2.2: Quaternions Flipped, ProxyArray, and Kit-less Training
  • AAS Units of Measurement 3.0: The unitId Change That Breaks ECLASS Wiring

Leave a Comment and share if you find it helpful Reading the Article in IoT Digital Twin PLM Site

Home

Tag Cloud

AI Agents ai for science AI Models benchmark Biotech Cilium Cloud Native Data Engineering devops digital twin eBPF Edge AI edge computing Fact Check fintech humanoid robots iiot Industrial IoT industrial protocols Industry 4.0 inference iot IoT Protocols Kubernetes lakehouse LLM LLM inference manufacturing MCP MQTT NVIDIA NVIDIA Jetson Observability OPC UA Physical AI physics PLM RAG Robotics ROS2 ROS 2 semiconductors TSN tutorial Unified Namespace

Categories

  • AI 135
  • Architecture 17
  • Autonomous Science 7
  • aws 2
  • Azure 5
  • Business 7
  • Development 30
  • Digital Transformation 1
  • Digital Twin 39
  • Health 4
  • iiot 103
  • iot 16
  • Kubernetes 43
  • Network 5
  • Newsbeat 4
  • PLM 11
  • Science 56
  • Security 10
  • Tech 202
  • Uncategorized 2
Copyright 2026 — IoT Digital Twin PLM. All rights reserved. Sinatra WordPress Theme
Scroll to Top