Multimodal AI Deployment, Edge Vision, and Real-time 3D Perception
MultimodalFlow is a technical publication about multimodal AI, computer vision, edge AI deployment on Jetson devices, TensorRT optimization, stereo depth estimation, thermal-RGB fusion, and industrial inspection systems.
Featured Articles
Qwen3 30B vs Gemma 4 26B on RTX 3090: Full VRAM Showdown
Head-to-head benchmark of Qwen3 30B and Gemma 4 26B on a single RTX 3090 with full VRAM available. Real Ollama API measurements — generation speed, prefill speed, and which model wins at each task.
Jetson Thor vs Jetson Orin 64GB: Local LLM Benchmark Notes
Hands-on local LLM benchmark notes from Jetson Thor and Jetson Orin 64GB, including Qwen model setup, latency, tokens per second, and practical deployment advice.
LocateAnything-3B Benchmark: RTX 3090 vs Jetson AGX Thor
Real hardware benchmark of NVIDIA's LocateAnything-3B vision-language model across RTX 3090 and Jetson AGX Thor — inference speed, memory, and deployment conclusions for edge AI.
Latest Articles
View all articles →Jetson AGX Thor + Orbbec 3D Camera: Building a Local Multimodal Robot Inspection System
A hands-on retrospective of a 3D robot inspection project built on NVIDIA Jetson AGX Thor with Orbbec Gemini 345Lg / RealSense D435i, point clouds, YOLO, VLM, RAG, and a local LLM.
Jetson Device Skills: Agentic Edge AI on AGX Orin 64GB — Install & Live Test
NVIDIA's open-source Jetson Device Skills give Claude Code and other agents native Jetson workflows. Hands-on install and benchmark on a real AGX Orin 64GB.
Qwable-v1 on NVIDIA Thor: Full Deployment, Testing & Evaluation
End-to-end guide for deploying Qwable-v1 (Qwen3.6-35B MoE + Claude Fable-5 distill) on NVIDIA Jetson AGX Thor — download, SGLang serving, Web UI, and closed-loop Agent benchmark. All numbers from live hardware.
Clustering Two NVIDIA DGX Spark Systems: 200GbE Looks Fast, but NCCL Tells the Real Story
Two DGX Spark nodes connected over 200GbE / ConnectX-7 RDMA. Raw ib_write_bw hits ~197 Gb/s, but real NCCL collective bandwidth lands at 10 GB/s — and here's exactly why they differ.
Qwen3.6-27B on RTX 3090 vs Jetson Thor: June 2026's Best Dense Coding Model, Benchmarked
Qwen3.6-27B outperforms 397B MoE models on coding benchmarks and fits on a single RTX 3090. Real inference numbers on RTX 3090 (Ollama) and Jetson Thor (llama.cpp) — and why June 2026's hottest models like Kimi K2.7-Code need very different hardware.
Edge AI Deployment Checklist for Small Teams in 2026
A practical checklist for deploying AI inference systems on edge hardware — covering hardware selection, model optimization, thermal management, monitoring, and the failure modes that only appear in production.
TensorRT for Computer Vision: What Actually Speeds Up Inference on Jetson and Desktop GPUs
A practical guide to TensorRT optimization for vision models — what it does, where the real speedups come from, which layers benefit most, and how to avoid the common pitfalls when deploying on Jetson or RTX GPUs.
Qwen3 30B vs Gemma 4 26B on RTX 3090: Full VRAM Showdown
Head-to-head benchmark of Qwen3 30B and Gemma 4 26B on a single RTX 3090 with full VRAM available. Real Ollama API measurements — generation speed, prefill speed, and which model wins at each task.
Gemma 4 on RTX 3090: 4B vs 12B vs 26B Benchmark — What Happens When VRAM Runs Out
Live benchmark of all three Gemma 4 sizes (4B, 12B, 26B) on a single RTX 3090. Shows how VRAM overflow kills generation speed on the 26B model, and which size actually makes sense for 24GB cards.
Use Cases
Industrial Inspection
Detecting weld defects, fabric flaws, and surface anomalies using thermal-RGB fusion and vision models.
Robotics Perception
Real-time 3D environment understanding via stereo depth and point cloud for mobile robot navigation.
Thermal-RGB Fusion
Combining thermal infrared and visible-light imagery for night-time detection and temperature anomaly alerts.
Edge AI Deployment
Shipping TensorRT-optimized inference pipelines on Jetson AGX Thor, Jetson Orin, and similar edge hardware.
Real-time Point Cloud
Streaming depth-sensor data as live 3D point clouds via Three.js and WebGL in the browser.
Local GPU Inference
Running large language models on consumer GPUs (RTX 3090, etc.) with Ollama and llama.cpp — benchmark data included.