Portable AI: Building Once, Running Everywhere

Most existing AI applications are tightly coupled to specific model providers, runtimes, clouds, and chips, creating a significant dependency problem. This reliance on particular hardware or software stacks leads to technical debt that becomes increasingly difficult to manage as hardware economics evolve rapidly. Developers frequently struggle to move beyond a simple "works on my machine" mindset, hindering broader deployment.

"Most of the AI applications that's been built today are tightly tied to something or the other. So it could be a model provider, a runtime, a cloud, a chip and in fact all of it." said Kavya, highlighting the pervasive issue.

Historically, software development aimed to abstract away hardware complexities through layers like operating systems, virtual machines, and web technologies. The goal was to enable developers to focus solely on application logic rather than the underlying hardware. However, AI's expansion beyond data centers is forcing a re-evaluation of these abstraction layers, as the unique demands of AI challenge established portability solutions.

"For most of the software history, we have been trying to make the sentence irrelevant. And instead of the sentence going into irrelevancy, we're actually seeing it compounding in the last few years," stated Prashant Sharma, indicating a regression in portability efforts.

AI is rapidly escaping the confines of data centers, migrating to laptops, smartphones, web browsers, vehicles, and a growing array of edge devices. This includes cameras, robots, wearables, and industrial systems, transforming computing into a more ambient and pervasive experience. This broad expansion creates intricate new requirements for developers building and maintaining portable AI applications across diverse hardware.

"Because AI is rapidly escaping the data center. It's moving on to the laptops, onto our phones, into the browser, into vehicles, into cameras, robots, variables, industrial systems, and increasingly edge devices," explained Prashant Sharma, outlining the scope of AI's decentralization.

The core industry goal for AI applications is to enable building an intelligent application once and running it universally without extensive modifications. This means eliminating the need to rewrite or rebuild the entire software stack for each new platform. Moreover, it aims to remove the burden of manual optimization for dozens of different processors, striving for true portability across the entire hardware landscape.

Prashant Sharma articulated this vision, asking, "Can I build an intelligent application once and run it everywhere? Not rewrite it. Not rebuild the entire software stack. Not optimize it manually by 20 different processors."

While developers might focus on whether an AI application can run across various environments, testers face a more profound challenge: ensuring it actually works correctly everywhere. This highlights a critical distinction between technical portability and functional reliability, as an application merely running does not guarantee it delivers the intended experience or performance across diverse hardware. The question for testers goes beyond simple execution to validate consistent behavior.

"If I'm a developer, I might ask, 'Can my application run everywhere?' And but if I'm a tester, I have to ask a much harder question. If it runs everywhere, does it still work everywhere?" emphasized Prashant Sharma, clarifying the expanded scope for testing.

The testing matrix for AI applications becomes intimidatingly complex due to multiple interacting variables. This includes various application types, such as voice assistants and embedded language models, deployed across diverse targets like cloud, laptops, and specialized embedded devices. The intricate combination of frameworks, runtimes, operating systems, CPUs, GPUs, MPUs, and various precision formats creates a combinatorial explosion of test scenarios.

Prashant Sharma described this challenge: "our model matrix starts to look something like model plus times framework times runtimes times operating system times OS times CPU cross GPU MPU precision devices. That's a pretty intimidating test matrix, if you will."

An AI application might run and complete inference without crashing, yet still fail to deliver an acceptable user experience. For instance, an inference speed dropping from 600 tokens per second to merely 15 tokens per second indicates a critical failure in optimization, even if the model technically functions. This means functional tests alone are insufficient to guarantee a quality user experience.

Prashant Sharma explained, "Historically, software portability was largely binary. Does the application run? Yes, no. AI introduces a much more uncontrollable possibility. The application runs, it doesn't crash. The model loads, the inference completes. Now, the projected inference time, instead of 600 tokens a second, drops to 15 tokens a second."

A critical example of performance-based failure is a 300-millisecond gap in GPS signal encoding on a smartwatch, which can lead to significantly inaccurate mapping. This latency isn't a crash, but it renders the application practically unusable for its intended purpose. Hardware constraints, such as increased memory consumption or thermal throttling, directly impact optimization and can fundamentally alter an application's behavior. Traditional binary testing struggles to capture these performance-driven failures, as the application technically runs but provides a poor user experience.

"there is a 300 millisecond gap um between by the time it picks up a GPS signal to by the time it encodes it on the on the hardware. So, you know, memory consumption doubles, the device starts thermal throttling, optimization changes. Does it run? Is that a binary answer anymore?" posed Prashant Sharma, underscoring the shift from binary to qualitative assessment.

Factors such as available operators, runtime implementations, numerical precision, and memory behavior can all diverge across platforms. This means what appears as a single AI application can effectively become multiple execution environments, each behaving uniquely. Therefore, testing must go beyond mere functionality to evaluate quality, latency, and sustained performance across all target devices.

Prashant Sharma noted, "The underlying hardware is different. Available operators may be different. The runtime implementation may be different. Uh you uh the numerical precision, memory behavior would be different. And suddenly, something that looks like a single AI application like as a monolith becomes like a multiple execution environment."

True portability for AI does not necessarily mean identical execution across all environments; rather, the goal is to deliver an acceptable experience everywhere. This distinction is crucial because different contexts have varying priorities: cloud environments optimize for throughput, while smartphones prioritize battery life. Embedded systems, for instance, face far more severe memory constraints than richer computing environments. Therefore, testing must understand and adapt to the specific context in which the AI is operating to ensure a tailored, acceptable user experience.

"perhaps the goal really isn't can AI behave identically everywhere? Perhaps the real goal is can we make AI deliver an acceptable experience everywhere? Now, that means testing needs to understand the context it is um broadly operating in," Prashant Sharma proposed, shifting the focus from uniformity to context-aware quality.

The industry is increasingly building sophisticated layers of abstraction between applications and their underlying hardware. These layers aim to manage the escalating complexity of compute environments, encompassing what can be described as five distinct layers of modern computing. This approach is designed to make the underlying hardware infrastructure less of an obstacle for developers, allowing them to focus more on application logic and less on low-level hardware intricacies.

The modern AI software stack includes multiple layers: applications, models, runtimes, and various IP classes such as CPUs, GPUs, and NPUs. Ideally, developers should not be burdened with manually orchestrating every thread or managing hardware at a granular level. The trend is for developers to focus on the application experience, with modern runtimes and compilers increasingly handling the complexities of hardware, including memory management and efficiency. This abstraction empowers developers to create more efficiently without deep hardware expertise.

"The developer ideally shouldn't have to manually orchestrate every single thread on that layer," stated Prashant Sharma, advocating for greater abstraction.

As more complexity is hidden from developers through abstraction layers, the burden of ensuring quality shifts significantly to automated validation processes. Something must still verify that all these intricate layers work seamlessly together. The definition of 'correct' software has evolved beyond simple pass-fail metrics, especially for AI, which introduces a 'multi-dimensional quality envelope' that transcends traditional binary results and demands more sophisticated validation. Automated testing is becoming indispensable in this context.

Prashant Sharma emphasized, "Now, the more complexity we hide from developers, the more responsibility move into validation because something still needs to verify all those layers work together."

Optimization often involves tension between a model's quality, performance, and efficiency. For example, a slight drop in accuracy might be acceptable if it leads to significant gains in speed or power consumption, depending on the application context. Key factors to consider include correctness, performance, efficiency, perceived quality, and portability.

Prashant Sharma articulated this approach, stating, "We are evaluating a multi-dimensional I would call it the multi-dimensional quality envelope, if you will."

These advanced frameworks are designed to deploy applications across a variety of representative real or simulated hardware targets. They meticulously measure critical metrics such as accuracy, memory usage, latency, power consumption, and thermal throttling. This evolution means testing is no longer just about identifying failures but about helping developers understand and manage the complex trade-offs inherent in multi-platform AI development.

"Testing isn't simply detecting failures. It's helping developers navigate the tradeoffs," explained Prashant Sharma, highlighting the expanded role of testing.

Developers should primarily focus on defining the desired user experience and application intent, rather than managing intricate hardware complexities. For instance, a developer might specify a requirement for inference under 100 milliseconds, or memory usage below a certain threshold, or power consumption within an acceptable range. The sophisticated testing infrastructure should then be responsible for ensuring these requirements are met across various target hardware environments, preventing developers from having to become hardware experts.

Prashant Sharma elaborated, "So, a developer defines the experience they need. So, for example, a developer may come to you and may ask you, 'I want the inference under 100 milliseconds, and I want memory below a certain threshold or power within an acceptable range.'"

AI is spreading rapidly into web browsers, personal computers, cars, cameras, factories, and robots, marking a significant shift in its deployment paradigm. This decentralization dramatically increases the number and diversity of execution environments for AI applications. This trend reflects the historical evolution of computing, which has consistently moved towards creating layers of abstraction to manage increasing complexity and distribute processing power.

Achieving quality and reliability in portable AI necessitates continuous verification across a diverse array of devices and environments. The traditional mantra of 'build once, run everywhere' is insufficient; instead, the focus must shift to 'build once, test everywhere, and then run anywhere with confidence.' This approach ensures that AI applications not only function but also maintain acceptable performance and user experience across their intended deployment targets. It emphasizes proactive and comprehensive testing as fundamental to the success of portable AI. This paradigm shift ensures that developers can trust their applications to deliver consistent value regardless of the underlying hardware.

Prashant Sharma concluded, "And for those of us thinking about quality, reliability, and testing, I think the more important idea is build once, test everywhere, and then run anywhere with confidence."