Achieve sub-10ms inference times by running AI models directly on devices. Eliminate network delays, enhance privacy, and enable real-time experiences that cloud AI simply cannot deliver.
Cloud-based AI introduces unavoidable latency that makes real-time applications impossible. Understanding where this latency comes from is the first step to eliminating it.
Network Round-Trip Time: 50-200ms depending on server location and network quality
Data Serialization/Transmission: 10-50ms to package and send image/video/sensor data
Queue Time: 20-100ms waiting for cloud GPU availability during peak loads
Model Inference: 10-100ms for the actual AI computation
Response Transmission: 10-50ms to send results back to device
Total Cloud AI Latency: 100-500ms (or more)
This makes real-time applications like AR, autonomous vehicles, industrial robotics, and live video analysis practically impossible.
Delays over 100ms are perceptible to users, creating frustrating lag in interactive applications and destroying the illusion of real-time response.
In autonomous systems and medical devices, 200-300ms delays can mean the difference between safe operation and catastrophic failure.
Cloud AI requires stable internet connections. Network jitter, dropouts, or congestion cause unpredictable performance degradation.
By running AI models directly on the device, we eliminate network latency entirely and achieve inference times measured in single-digit milliseconds.
Data Capture: 1-5ms from sensor/camera to memory
Preprocessing: 2-8ms for image/data normalization and formatting
Model Inference: 3-15ms on optimized mobile/edge hardware
Postprocessing: 1-5ms to format results
Total On-Device AI Latency: 7-33ms
That's 10-50x faster than cloud AI, enabling truly real-time applications.
We use advanced optimization techniques to make models small and fast enough for on-device deployment while maintaining high accuracy:
Modern devices include specialized AI accelerators that dramatically boost performance. We optimize for:
Apple Neural Engine, Qualcomm Hexagon DSP, Google Pixel Neural Core - achieving 5-10ms inference
NVIDIA Jetson, Google Coral TPU, Intel Movidius - optimized for computer vision and sensor fusion
ARM Cortex-M NPUs, Microchip FPGA accelerators - AI on microcontrollers with sub-5ms latency
Custom ASICs and FPGAs for ultra-low latency (under 1ms) in safety-critical applications
We leverage platform-optimized AI frameworks for maximum performance:
AR requires sub-20ms latency for smooth, realistic overlays. On-device AI enables real-time object detection, scene understanding, and pose estimation without perceptible lag.
Self-driving cars need to detect pedestrians, vehicles, and obstacles in real-time. On-device AI processes camera and LIDAR data in 10-30ms for safe decision-making.
Robotic arms require instant vision feedback for precise manipulation. On-device AI provides 5-15ms object detection and grasp prediction for high-speed assembly lines.
Real-time video filters, background replacement, and enhancement require frame-by-frame AI processing at 30-60 FPS (16-33ms per frame).
AI-powered NPCs, procedural generation, and player behavior analysis need instant responses to maintain immersion and gameplay flow.
Surgical robotics and patient monitoring require instantaneous AI analysis. On-device processing ensures under 20ms latency for life-critical decisions.
| Use Case | Cloud AI | On-Device AI | Improvement |
|---|---|---|---|
| Face Recognition | 150-300ms | 8-15ms | 15-20x faster |
| Object Detection | 200-400ms | 12-25ms | 12-16x faster |
| Speech Recognition | 100-250ms | 15-40ms | 6-10x faster |
| Text Analysis | 80-200ms | 5-12ms | 10-16x faster |
With proper optimization techniques, we typically maintain 95-99% of the original model's accuracy. In many cases, the accuracy difference is imperceptible for end users. We benchmark thoroughly against your requirements and only deploy models that meet accuracy thresholds while delivering target latency.
Most computer vision, NLP, and sensor-based models can be optimized for on-device deployment. Very large models like GPT-4 or complex video generation models still require cloud resources. However, for 80% of real-time AI use cases, on-device deployment is feasible and preferable. We help assess which models are candidates for on-device vs hybrid edge-cloud architectures.
Modern smartphones (iPhone 8+, Android with Snapdragon 660+), tablets, edge computing devices (Jetson, Coral), drones, smart cameras, and even some microcontrollers can run optimized AI models. We design solutions based on your target hardware constraints, whether it's flagship smartphones or resource-constrained embedded systems.
We implement over-the-air (OTA) update mechanisms that download optimized models when devices are connected to Wi-Fi. Updates can be incremental (downloading only changed parameters) or full model replacements. We also support A/B testing where new models run in shadow mode before replacing production models.
Our optimization techniques significantly reduce power consumption. Using hardware accelerators (Neural Engine, GPU delegates) is 5-10x more power-efficient than CPU inference. Quantized models require fewer memory operations, further reducing battery drain. Typical on-device AI adds under 5% to total battery consumption for most applications.
Our AI optimization experts will assess your models, identify optimization opportunities, and deliver on-device solutions that achieve sub-20ms latency while maintaining accuracy.
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