Presented by Arm
A simple software stack is the key to portable, scalable AI in the cloud and at the 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 change is afoot. The integrated toolchain and optimized libraries are making it possible to deploy models on all platforms without compromising performance.
Yet a significant hurdle remains: software complexity. Disparate devices, hardware-specific customizations, and layered technology stacks continue to hinder progress. To unlock the next wave of AI innovation, the industry must decisively move away from secret development toward streamlined, end-to-end platforms.
This change is already taking shape. Major cloud providers, edge platform vendors, and the open-source community are uniting on a unified toolchain that simplifies development and accelerates deployment from cloud to edge. In this article, we’ll explore why simplification is the 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
The issue is not just hardware diversity; It’s this repeated effort across frameworks and goals that slows down time-to-value.
Miscellaneous Hardware Targets: GPUs, NPUs, CPU-only devices, mobile SoCs, and custom accelerators.
tooling and framework fragmentation: TensorFlow, PyTorch, ONNX, MediaPipe, and others.
side obstacles: Devices require real-time, energy-efficient performance with minimal overhead.
according to Gartner ResearchThese mismatches create a major bottleneck: more than 60% of AI initiatives stall before production, driven by integration complexity and performance variability.
What does software simplification look like
Simplification is uniting around five steps that cut re-engineering costs and risks:
Cross-platform abstraction layers Which reduces re-engineering when porting models.
performance-optimized library Integrated into major ML frameworks.
integrated architectural design That scales from datacenter to mobile.
Open standards and runtime (for example, ONNX, MLIR) to reduce lock-in and improve compatibility.
Developer-first ecosystem Emphasizing speed, reproducibility and scalability.
These changes are making AI more accessible, especially to startups and academic teams that previously lacked the resources for complex optimization. Projects like Hugging Face’s Optimum and MLPerf benchmarks are also helping to standardize and validate cross-hardware performance.
Ecosystem dynamics and real-world signals Simplification is no longer aspirational; This is 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. Major ecosystem players are driving this change by aligning hardware and software development efforts, providing tight 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 has increased the 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 compute platforms and software toolchains, helping developers accelerate time-to-deployment without sacrificing performance or portability. The emergence of multi-model and general purpose foundation models (e.g., LLAMA, Gemini, Cloud) has also increased urgency. These models require flexible runtimes that can scale across cloud and edge environments. AI agents that interact, adapt, and execute tasks autonomously drive the need for high-efficiency, cross-platform software.
MLPerf Inference v3.1 includes over 13,500 performance results from 26 submitters, validating multi-platform benchmarking of AI workloads. The results span both data center and edge devices, demonstrating the diversity of optimized deployments now being tested and shared.
Overall, these signals make it clear that market demand and incentives are aligning around a common set of priorities, including maximizing per-watt performance, ensuring portability, minimizing latency, and providing security and stability at scale.
What must happen for successful simplification
To realize the promise of simplified AI platforms, several things must happen:
Strong hardware/software co-design: Hardware features that are exposed in the software framework (for example, matrix multipliers, accelerator instructions), and conversely, software that is designed to take advantage of the underlying hardware.
Consistent, robust toolchain and libraries:Developers need reliable, well-documented libraries that work across all devices. Performance portability is only useful if the devices are stable and well supported.
open ecosystem: Hardware vendors, software framework maintainers, and model developers need to cooperate. Standard and shared projects help avoid re-inventing the wheel for each new tool or use case.
Abstractions that do not obscure performance: While high-level abstraction helps developers, they still need to allow tuning or visibility where necessary. The right balance between abstraction and control is important.
Security, privacy and trust are built-in:Especially as more computing moves to devices (edge/mobile), issues such as data security, secure execution, model integrity and privacy matter.
Weapons as an example of ecosystem-led simplification
Simplifying 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 a variety of environments, from cloud computing clusters to battery-constrained edge devices. It also reduces the overhead of custom customization, making it easier to bring new products to market faster. Arm (NASDAQ: ARM) is pursuing this model with a platform-centric focus that drives hardware-software optimization through the software stack. But Computex 2025Arm demonstrated how its latest Arm9 CPUs, combined with AI-specific ISA extensions and Klidi libraries, enable tight integration with widely used frameworks such as PyTorch, ExecuTorch, ONNX Runtime, and MediaPipe. This alignment reduces the need for custom kernels or hand-tuned operators, allowing developers to unlock hardware performance without leaving familiar toolchains.
The real-world implications are significant. In the data center, Arm-based platforms are delivering better performance per-watt, which is critical for sustainably scaling AI workloads. On consumer devices, these optimizations enable ultra-responsive user experiences and background intelligence that is always-on, yet energy efficient.
More broadly, the industry is rallying around simplification as a design imperative, embedding AI support directly into hardware roadmaps, optimizing for software portability, and standardizing support for mainstream AI runtimes. Arm’s approach demonstrates how deep integration into the compute stack can make scalable AI a practical reality.
Market Validation and Momentum
In 2025, Nearly half of the compute shipped to major hyperscalers 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 better performance-per-watt and support seamless software portability. This development marks a strategic pivot towards energy-efficient, scalable infrastructure optimized for the performance and demands of modern AI.
At the edge, Arm-compatible inference engines are enabling real-time experiences, such as live translation and always-on voice assistants, on battery-powered devices. These advancements bring powerful AI capabilities directly to users without compromising energy efficiency.
The developer pace is also increasing. In a recent collaboration, GitHub and Arm introduced native Arm Linux and Windows Runners for GitHub Actions, which streamlines CI workflows for Arm-based platforms. These tools lower the barrier of entry for developers and enable more efficient, cross-platform development at scale.
what comes next
Simplification does not mean removing complexity completely; This means managing it in ways that empower innovation. As AI stacks stabilize, the winners will be those that deliver seamless performance in a fragmented landscape.
From a future perspective, expect:
Benchmark as guardrail: MLPerf + OSS suites guide where to optimize next.
More upstream, fewer forks: Hardware features land in mainstream devices, not custom branches.
Convergence of Research + Production: Fast handoff from papers to product through shared runtime.
conclusion
The next phase of AI isn’t about exotic hardware; It’s also about software that travels well. When a single model scales efficiently across the cloud, client, and edge, teams ship faster and spend less time rebuilding the stack.
Ecosystem-wide simplification, not brand-based slogans, will differentiate the winners. The practical playbook is clear: integrating platforms, optimizing upstream, and measuring with open benchmarks. Learn how the Arm AI software platform Enabling this future – efficiently, safely and at scale.
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