What is On device Inference?
The practice of executing machine learning (ML) models directly on a device such as a laptop, tablet, smartphone, or Internet of Things (IoT) device instead of depending on cloud servers for computation is known as “on device inference.” With this method, AI models may produce outputs locally, categorise data, and make predictions without requiring an internet connection.
Important Advantages of on device Inference
Reduced Latency
There is no waiting for a response after transmitting data to the cloud since the processing takes place locally.
Enhanced Security & Privacy
By keeping sensitive data on the device, any security threats are minimised.
Offline Functionality
Because AI functions don’t require an internet connection, they are more dependable in places with weak networks or distant locations.
Reduced Cloud Costs
Reduces costs for consumers and organisations by doing away with the requirement for continuous data transfer to cloud servers.
Efficiency of Power
Battery life can be increased by using optimised models that operate effectively on contemporary devices like GPUs and NPUs (Neural Processing Units).
The tech sector has been impacted by the release of DeepSeek R1, a state-of-the-art reasoning AI model. This is because its performance challenges the traditional knowledge around Artificial Intelligence development and is on par with or better than state-of-the-art alternatives.
This crucial moment is a part of a larger trend that highlights the creativity in developing high-quality multimodal reasoning and Small language models, as well as how they’re getting Artificial Intelligence ready for on device inference and commercial applications. These new models’ device compatibility speeds up scalability and increases demand for potent edge chips.
The quality, performance, and efficiency of AI models that can now operate on devices have dramatically improved due to four key themes that are driving this shift:
State-of-the-art smaller AI models have superior performance
Innovative models can already outperform Large models from a year ago that could only function on the cloud with innovative approaches like model distillation and unique AI network topologies that streamline the development process without compromising quality.
Model sizes are decreasing rapidly
Modern quantisation and pruning methods enable developers to shrink models without significantly affecting accuracy.
Developers have more to work with
AI is now prepared for large-scale commercial applications across the edge with the quick spread of high-quality AI models, which have made capabilities like text summarisation, coding assistants, and live translation ubiquitous in gadgets like smartphones.
AI is becoming the new user interface
Across a range of applications, personalised multimodal AI agents will streamline interactions and competently finish tasks.
The shift from AI training to large-scale inference and the extension of AI computational processing from the cloud to the edge are two areas in which Qualcomm Technologies is well-positioned to take the lead and profit. The company’s successes include custom CPUs, NPUs, GPUs, and low-power subsystems. The company’s relationship with model builders and tools, frameworks, and SDKs for deploying models across edge device segments help developers accelerate AI agent and app adoption.
The impending transition in the AI environment towards large-scale inference is confirmed by the recent disruption and revaluation of the training process for AI models. It will start a fresh cycle of innovation and advancement in edge inference computing. Although training will still take place in the cloud, inference will profit from the large number of Qualcomm-powered devices and generate demand for further edge-based AI-enabled processors.
The era of AI inference innovation is here
Because there are so many high-quality, smaller models available, inference workloads where apps and services employ the models to benefit companies and consumers are receiving more attention.
In order to facilitate the commercialisation of the newest generation of AI-focused Copilot+ PCs, Qualcomm Technologies has worked on optimising a number of AI models.
The company has also collaborated with Samsung and Xiaomi to make AI-enabled flagship smartphones.
Device-wide AI inferencing has enabled the development of generative AI apps and assistants. These days, typical capabilities include real-time language translation, AI-generated and edited images, and document summarisation. AI is used by camera applications for real-time scene optimisation, object identification, and computational photography.
The creation of multimodal apps, which integrate many data formats text, visual, audio, and sensor input to provide more comprehensive, contextually aware, and customised experiences, comes next. AI assistants can now transition between communication modes and provide multimodal outputs with the Qualcomm AI Engine, which combines the power of specially designed NPUs, CPUs, and GPUs to optimise such operations on-device.
The core of the upcoming generation of user interfaces is agentic AI. By anticipating user requirements and proactively carrying out intricate processes within devices and apps, AI systems are able to make decisions and handle tasks. These agents may operate constantly and safely within the devices because to Qualcomm Technologies’ emphasis on effective, real-time AI processing. They rely on a personal knowledge graph that precisely identifies the user’s preferences and wants, eliminating the need for the cloud. With the use of natural language and picture, video, and gesture-based interactions, these developments are gradually establishing the foundation for AI to take over as the main user interface.
Qualcomm Technologies is also well-positioned for the embodied AI age, which will see robotics include AI capabilities. Qualcomm Technologies hopes to enable accurate interactions in dynamic, real-world situations by utilising its inference optimisation capabilities to fuel real-time decision-making for robots, drones, and other autonomous devices.
Even though many AI models are trained on the cloud, smaller, distilled models may be used on devices in a matter of days or weeks. For instance, DeepSeek R1-distilled models were operating on Snapdragon-powered PCs and smartphones.
By using local data to offer more context, deploying inference within devices improves privacy, addresses immediacy through decreased latency, and allows continuous operation of AI features and apps. By reducing fees related to cloud inference services, it also lowers expenses for developers and/or customers. Software and service vendors are encouraged to implement inference AI at the edge.
