Presented by Arm
A simple software stack is the key to portable, scalable AI in the cloud and edge.
AI is now powering real-world applications, yet fragmented software stacks are holding it back. Developers routinely rebuild the same models for different hardware targets, wasting time gluing code instead of shipping features. The good news is that a shift is underway. Unified toolchains and improved libraries are making it possible to deploy models across platforms without compromising performance.
Yet one major hurdle remains: software complexity. Different tools, hardware improvements, and layered tech stacks continue to hinder progress. To unlock the next wave of AI innovation, the industry must shift decisively away from siled development and toward seamless, end-to-end platforms.
This change is already taking shape. Major cloud providers, edge platform vendors, and open source communities are shifting to unified toolchains that simplify development and accelerate cloud-to-edge deployments. In this article, we’ll explore why simplicity is key to scalable AI, what’s driving this momentum, and how next-gen platforms are turning that vision into real-world results.
Disruption: fragmentation, complexity and inefficiency
The problem isn’t just the type of hardware. It’s the duplication of effort in frameworks and goals that slows down value over time.
Diverse hardware targets: GPU, NPU, CPU only devices, mobile SOCS, and custom accelerators.
Piece of tooling and framework: TensorFlow, PyTorch, ONNX, MediaPipe, and others.
Edge barriers: Devices require real-time, energy-efficient performance with minimal overhead.
According to Gartner Researchthese similarities create a key bottleneck: 60 percent of AI initiatives stall before production, driven by integration complexity and performance variability.
The simplicity of the software looks like
Simplicity revolves around five moves that reduce reengineering costs and risk:
Cross-platform abstraction layers which minimizes re-engineering when porting models.
Performance driven libraries Integrated into major ML frameworks.
Unified Architectural Designs From data center to mobile at scale.
Open standards and runtimes (eg, ONNX, MLIR) reducing lock-in and improving compatibility.
A developer’s first ecosystem Emphasizing speed, reproducibility, and scalability.
These shifts are making AI more accessible, especially to startups and academic teams that previously lacked resources for bespoke optimization. Projects such as FaceMax and Embrace the MLPRF benchmark are also helping to standardize and validate cross-hardware performance.
Ecosystem momentum and real-world indicators Simplicity is no longer desirable. It’s happening now. Across the industry, software considerations are influencing decisions at the IP and silicon design levels, resulting in solutions that are production-ready from day one. Major ecosystem players are driving this change by aligning hardware and software development efforts, delivering tighter integration across the stack.
A key catalyst is the rapid rise of edge computing, where AI models are deployed directly on devices rather than in the cloud. This has fueled demand for security software stacks that support end-to-end optimization from silicon to system to application. Companies like ARM are responding by enabling tight coupling between their compute platforms and software toolchains, helping developers accelerate time-to-deployment without sacrificing performance or portability. The emergence of multimodal and general-purpose foundation models (eg, Llama, Gemini, Cloud) has also added urgency. These models require flexible runtimes that can scale across cloud and edge environments. AI agents, which interact, adapt and perform tasks autonomously, further drive the need for high-performance, cross-platform software.
MLPRF Inference V3.1 includes more than 13,500 performance results from 26 submitters, validating multi-platform benchmarking of AI workloads. The results spanned both the data center and edge devices, demonstrating a diversity of better deployments and now shared commonality.
Taken together, these indicators illustrate that market demand and incentives are aligning around a common set of priorities, including maximizing performance per watt, ensuring portability, minimizing latency, and delivering security and consistency at scale.
What should happen for successful simplicity?
To realize the promise of simple AI platforms, several things are essential:
Co-design robust hardware/software: hardware features that are exposed in software frameworks (eg, matrix multipliers, accelerator instructions), and conversely, software that is designed to take advantage of the underlying hardware.
Consistent, robust toolchains and libraries: Developers need reliable, well-documented libraries that work across devices. Performance portability is only useful if the tools are stable and well supported.
Open ecosystem: Hardware vendors, software framework maintainers, and model developers need to collaborate. Standards and common projects help avoid reinventing the wheel for each new tool or use case.
Abstracts that do not obscure performance: While high-level abstraction helps developers, they must allow tuning or visibility wherever needed. The right balance between abstraction and control is key.
Built in security, privacy, and trust: Especially as more compute shifts to devices (edge/mobile), issues like data protection, safe execution, model integrity, and privacy issues.
Arms as an example of ecosystem-led facilitation
Facilitating AI at scale now depends on system-wide design, where silicon, software, and developer tools evolve in lockstep. This approach enables AI workloads to run efficiently in diverse environments, from cloud diagnostics clusters to battery-connected edge devices. It also reduces the overhead of bespoke optimization, making it easier to bring new products to market faster. ARM (NASDAQ: ARM ) is advancing this model with a platform-based focus that drives hardware-software optimization through the software stack. at Computex 2025ARM demonstrated how its latest ARM9 CPUs, combined with AI-specific ISA extensions and keyed libraries, enable tight integration with widely used frameworks such as PyTorch, Executor, ONX Runtime, and MediaPipe. This alignment reduces the need for custom kernels or manual operators, allowing developers to unlock hardware performance without abandoning familiar toolchains.
The real-world implications are significant. In the data center, ARM-based platforms are delivering better performance, which is critical to sustainably scale AI workloads. On consumer devices, these improvements enable highly responsive user experiences and background intelligence that is always-on, yet power-efficient.
More broadly, the industry is revolving around simplicity as a design imperative, embedding AI support directly into the hardware roadmap, optimizing for software portability, and standardizing support for mainstream AI runtimes. Arm’s approach illustrates how deep integration into the compute stack can make scalable AI a practical reality.
Market validation and speed
In 2025, About half of the compute delivered to large hyperscalars will run on ARM-based architecturesa milestone that marks a significant shift in cloud infrastructure. As AI workloads become more resource-intensive, cloud providers are prioritizing architectures that deliver every watt on improved performance and support seamless software portability. This evolution marks a strategic shift toward energy-efficient, scalable infrastructure for the performance and demands of modern AI.
At the edge, arm-compatible inference engines are enabling real-time experiences like live translation and always-on voice assistants on battery-powered devices. These advances bring powerful AI capabilities directly to consumers, without sacrificing energy efficiency.
The developer speed is also accelerating. In a recent collaboration, GitHub and ARM introduced native Arm Linux and Windows runners for GitHub Actions, and streamlined CI workflows for Arm-based platforms. These tools lower the barrier to entry for developers and enable more efficient, cross-platform development at scale.
What comes next?
Simplicity does not mean removing complexity altogether. That means handling it in ways that empower innovation. As the AI stack stabilizes, the winners will be those that offer smooth performance in a fragmented landscape.
From a future-facing perspective, expect:
Benchmarks as protectors: MLPERF + OSS Suites Guide Where to Improve Next
Further upstream, fewer forks: Hardware features land in mainstream tools, not custom branches.
Research Exchange + Production: Faster handoff from documents to product through shared runtime.
The result
The next phase of AI is not about exotic hardware. It’s also about software that travels well. When a single model lands efficiently on the cloud, client, and edge, teams ship faster and spend less time rebuilding the stack.
Ecosystem-wide simplicity, not brand-led slogans, will separate the winners. The practical playbook is clear: scale with platforms, upstream optimizations, and open benchmarks. Discover how the ARMA software platform is Enabling that future – efficiently, securely and at scale.
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