Modern Physical AI systems are distributed pipelines. The goal is not simply higher FPS; it is predictable latency, controlled memory use, reasonable power consumption, and reliable behavior.
Sensors / Smart Glasses
↓
Kotlin / Flutter
↓
Network / Gateway
↓
NVIDIA Jetson
↓
ROS 2 / Isaac ROS
↓
NVIDIA AI Model
↓
Planner / Controller
Before changing code, record:
Keep the test scenario identical between benchmark runs.
Measure each stage separately:
Capture → Transfer → Decode → Preprocess → Inference → Postprocess → UI
Optimize the stage contributing the most latency instead of optimizing arbitrary code.
Do not automatically process every sensor event.
private var busy = false
fun onFrame(frame: Frame) {
if (busy) return
busy = true
executor.execute {
try {
process(frame)
} finally {
busy = false
}
}
}
For real-time perception, processing the newest frame can be preferable to accumulating stale frames.
Use different paths for different priorities:
High priority → robot commands / safety
Medium → perception / navigation
Low priority → analytics / logging / cloud upload
A large video upload should never block a safety command.
Watch for pipelines such as:
Camera → YUV → RGB → Bitmap → JPEG → Base64
Every conversion can consume CPU, memory, and time. Keep data in an appropriate native representation for as long as possible.
An unlimited queue can turn a temporary overload into seconds of stale latency.
Use a small buffer or latest-frame strategy for time-sensitive perception.
For Android/Flutter, profile release/profile builds with the platform's performance tools. For Jetson, measure CPU, GPU, memory, temperature, and sustained behavior under the complete robotics workload.
A 30-second benchmark can hide thermal throttling or memory pressure. Run longer tests and record the performance curve.
Include:
The system should degrade gracefully.
Version | FPS | Latency | RAM | GPU | Temp
--------|-----|---------|-----|-----|-----
Before | 20 | 120 ms | 2GB | 55% | 58C
After | 28 | 75 ms | 1.7GB | 61% | 60C
Use your actual measurements rather than relying on synthetic numbers.
This tutorial focuses specifically on FP32/FP16/INT8 comparison, engine warmup, throughput, latency and accuracy validation..
Recommended optimization sequence:
AI models should normally produce validated perception, plans, or intents. Deterministic safety and control layers should remain responsible for enforcing physical constraints.
AI output
↓
Validation
↓
Safety constraints
↓
Controller
↓
Actuators
Performance optimization across smart glasses, Flutter, Kotlin, NVIDIA Jetson, ROS 2, and Physical AI requires an end-to-end measurement strategy. Optimize latency, memory, bandwidth, GPU utilization, thermals, and reliability together rather than chasing a single benchmark number.
Website: www.v-modal.com
SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter
SDK Android: https://github.com/v-modal/vmodal_sdk_android
Discord: https://discord.gg/K72z28KUx