arm provided
A simpler software stack is the key to portable, scalable AI across the cloud and edge.
AI is currently powering real-world applications, but fragmented software stacks are holding it back. Developers regularly rebuild the same model for different hardware targets, wasting time gluing code together instead of shipping features. The good news is that change is afoot. An integrated toolchain and optimized libraries allow you to deploy models across platforms without compromising performance.
However, one important hurdle remains: software complexity. Disparate tools, hardware-specific optimizations, and layered technology stacks continue to bottleneck progress. To unleash the next wave of AI innovation, the industry must decisively move away from siled development and pivot toward streamlined end-to-end platforms.
This transformation is already taking shape. Leading cloud providers, edge platform vendors, and the open source community are coming together in a unified toolchain that simplifies development and accelerates cloud-to-edge deployment. In this article, we explore why simplification is key to scalable AI, what’s driving this momentum, and how next-generation platforms are turning that vision into real-world results.
Bottlenecks: fragmentation, complexity, and inefficiency
It’s not just the type of hardware that matters. Duplicate work across frameworks and targets slows time to value.
Diverse hardware targets: GPU, NPU, CPU-only devices, mobile SoCs, and custom accelerators.
Fragmentation of tools and frameworks: TensorFlow, PyTorch, ONNX, MediaPipe, etc.
edge constraints: Devices require real-time, energy-efficient performance with minimal overhead.
According to Gartner Research, these discrepancies create significant hurdles. More than 60% of AI initiatives stop before going live due to integration complexity and inconsistent performance.
What does software simplification mean?
Simplification is unified around five initiatives that reduce the cost and risk of reengineering.
Cross-platform abstraction layer Minimize re-engineering when porting models.
Performance-tuned library Integrated with major ML frameworks.
unified architectural design Extend from data center to mobile.
Open standards and runtimes (ONNX, MLIR, etc.) Reduce lock-in and improve compatibility.
Developer-first ecosystem Focus on speed, reproducibility, and scalability.
These changes have made AI more accessible, especially to startups and academic teams that previously lacked the resources for bespoke optimization. Projects such as Hugging Face’s Optimum and MLPerf benchmarks also help standardize and validate performance across hardware.
Ecosystem dynamics and real-world signals Simplification is no longer an aspiration. It’s happening now. Across the industry, software considerations are influencing decisions at the IP and silicon design level, resulting in solutions that are production-ready from day one. Key ecosystem players are driving this change by aligning their hardware and software development efforts and achieving tighter integration across the stack.
A key catalyst is the rapid rise of edge inference, where AI models are deployed directly on devices rather than in the cloud. This is driving demand for streamlined software stacks that support end-to-end optimization from silicon to system to application. Companies like Arm are responding by enabling tighter coupling between their computing platforms and software toolchains, helping developers accelerate time to deployment without sacrificing performance or portability. The emergence of multimodal and generic basic models (LLaMA, Gemini, Claude, etc.) is also gaining urgency. These models require flexible runtimes that can scale across cloud and edge environments. AI agents interact, adapt, and perform tasks autonomously, further increasing the need for highly efficient cross-platform software.
MLPerf Inference v3.1 includes over 13,500 performance results from 26 submitters, validating multi-platform benchmarks for AI workloads. Results spanned both data centers and edge devices, demonstrating the versatility of optimized deployments currently being tested and shared.
Taken together, these signals make it clear that market demands and incentives are aligning around a common set of priorities: maximizing performance per watt, ensuring portability, minimizing latency, and providing security and consistency at scale.
What it takes to successfully simplify
For the promise of a simplified AI platform to become a reality, several things need to happen.
Powerful hardware and software co-design: Hardware functionality exposed in a software framework (matrix multipliers, accelerator instructions, etc.), and conversely, software designed to take advantage of the underlying hardware.
Consistent and robust toolchain and libraries: Developers need reliable, well-documented libraries that work across devices. Performance portability is only useful if the tool is stable and well supported.
open ecosystem: Hardware vendors, software framework maintainers, and model developers need to work together. Standards and shared projects help avoid reinventing the wheel for each new device or use case.
Abstraction that doesn’t obscure performance: High-level abstractions are useful for developers, but they need to allow for adjustments and visualization as needed. The right balance between abstraction and control is important.
Security, privacy and trust are built in: Issues such as data protection, secure execution, model integrity, and privacy become important, especially as more computing moves to devices (edge/mobile).
Arm as an example of ecosystem-driven simplification
Simplifying AI at scale depends on the design of the entire system, where silicon, software, and developer tools evolve in tandem. This approach allows AI workloads to run efficiently across a variety of environments, from cloud inference clusters to battery-constrained edge devices. It also reduces the overhead of bespoke optimization, making it easier to bring new products to market faster. Arm (Nasdaq:Arm) is driving this model with a platform-centric focus that drives hardware and software optimization through the software stack. At COMPUTEX 2025, Arm demonstrated how the latest Arm9 CPU, combined with AI-specific ISA extensions and the Kleidi library, enables tight integration with widely used frameworks such as PyTorch, ExecuTorch, ONNX Runtime, and MediaPipe. This tuning reduces the need for custom kernels and hand-tuned operators, allowing developers to take full advantage of hardware performance without abandoning their familiar toolchains.
The real-world implications are significant. In the data center, Arm-based platforms deliver increased performance per watt, which is critical for sustainably scaling AI workloads. In consumer devices, these optimizations result in a highly responsive user experience and always-on, power-efficient background intelligence.
More broadly, the industry is rallying around simplicity as a design imperative, building AI support directly into the hardware roadmap, optimizing software portability, and standardizing support for mainstream AI runtimes. Arm’s approach shows how scalable AI can become a reality through tight integration across the compute stack.
Market validation and momentum
By 2025, nearly half of the compute shipped to major hyperscalers will be running on Arm-based architectures, a milestone that marks a major shift in cloud infrastructure. As AI workloads become more resource-intensive, cloud providers are prioritizing architectures that deliver superior performance per watt and support seamless software portability. This evolution marks a strategic shift toward energy-efficient and scalable infrastructure optimized for the performance and demands of modern AI.
At the edge, Arm-compatible inference engines enable real-time experiences like live translation and always-on voice assistants on battery-powered devices. These advances bring powerful AI capabilities directly to users without sacrificing energy efficiency.
Developer momentum is also accelerating. In a recent collaboration, GitHub and Arm introduced native Arm Linux and Windows runners for GitHub Actions to streamline 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 happens next
Simplification does not mean removing complexity completely. It means managing in a way that fosters innovation. Once the AI stack stabilizes, the winners will be those that deliver seamless performance across fragmented environments.
From a forward-looking perspective, we expect the following:
Benchmarks as guardrails: The MLPerf + OSS suite guides you where to optimize next.
More upstream means fewer forks: Hardware features are built into mainstream tools rather than in custom branches.
Fusion of research and production: Document-to-product handoff is accelerated through a shared runtime.
conclusion
The next stage of AI isn’t about exotic hardware. It’s also about software that moves comfortably. When the same model is efficiently deployed across the cloud, client, and edge, teams can ship faster and spend less time rebuilding their stacks.
Simplification of the entire ecosystem, not brand-driven slogans, will separate the winners. The practical strategy is clear. Platform integration, upstream optimization, and measurement with open benchmarks. See how the Arm AI software platform is enabling this future efficiently, securely, and at scale.
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