SiMa Modalix: The Undisputed YOLO Leader for Physical AI
Unmatched Performance and Efficiency
When it comes to deploying the highly popular YOLO (“You Only Look Once”) models for object detection in physical AI applications, SiMa.ai Modalix is the clear leader. Modalix not only delivers top-tier frames per second performance from YOLOv4 to the latest YOLOv12 models from Ultralytics, it does so with unmatched power efficiency.
Competing Physical AI Platforms Just Can’t Keep Up
On Modalix, object detection benchmarks show YOLOv8n running at 1,414 frames per second (FPS). That’s over 5x faster than the NVIDIA Jetson Orin NX platform, based on Ultralytics benchmark data combined with internal testing. That means more responsive robots, better image detection systems for industrial monitoring, and faster real-time multi-camera processing.
Figure 1: Modalix delivers 5x higher YOLOv8n performance than NVIDIA Jetson Orin NX
Source: SiMa data – internal study*
NVIDIA data – Ultralytics study
*SiMa internal study conducted at 640×640 resolution using Ultralytics YOLO reference models with INT8 quantization. All platforms assessed using commercially available hardware.
Leadership in Multi-Stream Video
SiMa Modalix advantages carry through to complex, multi-channel pipelined workloads as well – like 16-channel detection pipelines at VGA (45 FPS per channel) and HD (>20 FPS per channel). YOLOv8n object detection runs at 102 frames per second per Watt (FPS/W), which is 3.7x higher efficiency than NVIDIA Jetson Orin NX. That means customers can scale up to more streams without breaking power or thermal budgets.
Figure 2: Modalix delivers 3.7x better power efficiency than NVIDIA Jetson Orin NX
Source: SiMa data – internal study*
*SiMa internal study measured complete end-to-end pipeline performance including VGA video decode, inference, and encode across 16 channels running simultaneously.
Better by Design
Unlike machine learning (ML) accelerator cards which require external, power-hungry hosts for critical functions like video ingest, decoding, VMS software, and business logic, SiMa SoCs integrate the entire processing pipeline on-chip. This reduces complexity, cost, and power, making SiMa the best solution for mobile or power-constrained applications.
Infused into the SiMa Modalix SoC heterogeneous architecture is an 8-core 1.5GHz Arm A65 microprocessor that works in concert with our embedded Machine Learning Accelerator (MLA) block to efficiently manage all these tasks directly on-chip – keeping power low and performance high.
Figure 3: Modalix outperforms both SoC & ML accelerator systems in YOLOv8 efficiency
Source: Internal study*
*ML accelerator system measurements include external host power (measured as delta between idle and active processing). SiMa Modalix integrates all functions on-chip with no external host required.
Top Tier Accuracy
All of this benefit comes without compromise in accuracy. SiMa.ai maintains high detection and segmentation fidelity, so efficiency gains never come at the expense of reliability.
Figure 4: Modalix maintains high fidelity while delivering industry-leading performance
Source: SiMa internal study
Excellence That Scales Across the YOLO Family
The great performance extends across all supported YOLO models on Modalix (v4, v5, v7, v8, v9, v11, v12, X). And as new YOLO models come out, SiMa will continue to lead the physical AI space. For example, with YOLOv11n, Modalix maintains the same dominant performance, outpacing NVIDIA Jetson Orin NX by nearly 5x.
Figure 5: SiMa.ai maintains its performance advantage with YOLOv11
Source: SiMa data – internal study
NVIDIA data – Ultralytics study
Nobody YOLOs Better than SiMa!
Purpose-built for real-world performance in multi-channel video, robotics, and other critical physical AI applications, SiMa.ai Modalix delivers industry-leading throughput, efficiency, and accuracy in one compact 10 W platform.
Ready to deploy YOLO at scale?
Explore the Modalix SoC and Palette SDK, or contact us for a live demonstration.