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On-device AI

On-device AI

In an on-device AI use case, the goal is to run AI models directly on devices with limited hardware, such as smartphones, tablets, or IoT devices. Here, small language models (SLMs) offer clear advantages over large language models (LLMs) in terms of efficiency, speed, and resource usage.


Use Case: Voice Command Recognition for a Smart Home Device


Scenario

A company has developed a smart home assistant that processes voice commands to control lights, thermostats, and other connected devices. The assistant runs entirely on-device, meaning the AI model must operate within the hardware limitations of consumer electronics like smart speakers.


Key Metrics for Comparison

  • Latency: Time taken to process the voice command.

  • Resource Utilization: Memory, processing power, and battery consumption.

  • Inference Accuracy: The percentage of correctly interpreted commands.


Metric

  • Model Size

  • Latency (average)

  • Memory Usage (RAM)

  • CPU/GPU Requirements

  • Battery Consumption

  • Inference Accuracy


Small Language Model (SLM)

  • 30M parameters

  • 0.03 seconds/command

  • 150 MB

  • CPU only

  • 1.5% per hour (smartphone)

  • 92%


Large Language Model (LLM)

  • 1.3B parameters

  • 0.7 seconds/command

  • 6 GB

  • GPU/High-end CPU

  • 10% per hour (smartphone)

  • 96%


Technical Insights

  1. Latency: The SLM responds almost instantaneously (0.03 seconds) to user commands, providing a smooth, real-time experience. The LLM, on the other hand, introduces noticeable delays (0.7 seconds), which may feel sluggish in an interactive on-device environment, especially for users accustomed to instant responses from smart assistants.

  2. Memory and Compute Efficiency: SLMs are highly efficient in terms of memory usage, requiring just 150 MB of RAM. They can run seamlessly on low-end processors without the need for dedicated GPUs or high-performance CPUs, making them ideal for devices like smart home hubs or even smartphones. LLMs, with their 6 GB memory requirements, are far too resource-intensive for most on-device applications and typically require cloud-based inference.

  3. Power/Battery Efficiency: On mobile or battery-operated devices, power consumption is crucial. SLMs consume far less battery power, draining only 1.5% of the smartphone battery per hour. LLMs, by contrast, consume significantly more power (10% per hour), limiting the practical use of LLMs on battery-operated devices for extended periods.

  4. Inference Accuracy: While the LLM offers marginally better accuracy (96% vs. 92%), the difference may not justify the increased computational cost in real-time use cases like smart home devices, where commands are generally simple and context is clear.


Business Insights

  1. Cost Efficiency: Developing and deploying on-device AI using an SLM reduces infrastructure costs significantly because no cloud resources are needed for processing. With lower hardware requirements, companies can manufacture more affordable devices with lower operating costs. This could mean offering competitive pricing in the smart home market without sacrificing performance.

  2. Faster User Experience: The near-instant response time of an SLM (0.03 seconds) ensures a smoother user experience. In products where speed and responsiveness are key selling points, the faster latency of SLMs can enhance customer satisfaction, leading to higher adoption rates.

  3. Longer Battery Life: For battery-powered devices like smartphones, wearables, or smart home assistants, battery life is a critical selling point. With significantly lower battery consumption, SLMs allow for longer device operation without frequent charging, making the product more user-friendly and increasing the likelihood of user engagement.

  4. Scalability: Deploying SLMs on a wide range of consumer devices—such as home automation, wearables, or portable electronics—makes scaling easier. These models are lightweight, adaptable, and can run on even the most basic hardware, ensuring your AI product can reach a broader market without requiring cloud infrastructure.


Benchmarking Example

Let’s assume a smart home hub processes 10,000 voice commands per day.


  • SLM Processing Time: 0.03 seconds/command → 5 minutes/day total.

  • LLM Processing Time: 0.7 seconds/command → 2 hours/day total.


The SLM’s latency advantage allows for 24x faster processing of user commands while consuming significantly fewer resources.


Conclusion

In on-device AI use cases, such as voice command recognition for smart home devices, small language models (SLMs) offer clear advantages over large language models (LLMs). SLMs provide greater speed, lower memory, and power consumption, and adequate accuracy for everyday tasks, making them the best choice for deploying AI on constrained hardware. While LLMs may excel in more complex and nuanced tasks, their resource-intensive nature limits their applicability in on-device scenarios.


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