Simplifying the AI stack: The key to scalable, portable intelligence from cloud to edge

Submitted by Arm

A simpler software stack is the key to portable, scalable AI across the cloud and edge.

AI now powers real-world applications, but it is held back by fragmented software stacks. Developers routinely refactor the same models for different hardware targets, wasting time pasting code instead of shipping features. The good news is that the shift is underway. Unified toolchains and optimized libraries allow models to be deployed across platforms without compromising performance.

However, one critical hurdle remains: software complexity. Disparate tools, hardware improvements, and layered technology stacks continue to hinder progress. To unleash the next wave of AI innovation, the industry must decisively move away from siled development and toward simplified, comprehensive platforms.

This transformation is already taking shape. Leading cloud providers, edge platform vendors, and open source communities are converging on unified toolchains that simplify development and accelerate 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 turn this vision into real-world outcomes.

The bottleneck: fragmentation, complexity, and inefficiency

The problem is not just limited to the variety of devices; It’s duplicate effort across frameworks and goals that slows down the time to value.

Miscellaneous device goals: GPUs, NPUs, CPU-only devices, mobile SoCs, and custom accelerators.

Tools and frame segmentation: TensorFlow, PyTorch, ONNX, MediaPipe, and others.

Edge restrictions: Devices require energy-efficient performance in real-time and with minimal load.

According to Gartner research, these 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 clusters around five steps that reduce reengineering cost and risk:

Cross-platform abstraction layers Which reduces re-engineering when transferring models.

Performance tuning libraries It is integrated into major machine learning frameworks.

Unified architectural designs This range from data center to mobile.

Open standards and runtimes (e.g. ONNX, MLIR) which reduces locking and improves compatibility.

Developer ecosystems first With an emphasis on speed, reproducibility and scalability.

These shifts are making AI more accessible, especially for startups and academic teams that previously lacked the resources for custom optimization. Projects like Hugging Face’s Optimum and MLPerf standards also help standardize and validate performance across devices.

Ecosystem momentum and real-world signals Simplification is no longer an aspiration; It’s happening now. Across the industry, software considerations influence decisions at the IP and silicon design level, resulting in production-ready solutions from day one. Key players in the ecosystem are driving this transformation by aligning hardware and software development efforts, providing tighter integration across the group.

A major catalyst is the rapid rise in inference at the edge, where AI models are deployed directly on devices rather than in the cloud. This has increased demand for streamlined software packages 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 speed up time to deployment without sacrificing performance or portability. The emergence of multimodal and general-purpose foundation models (such as LLaMA, Gemini, Claude) has also increased the urgency. These models require flexible runtimes that can scale across cloud and edge environments. AI agents, which interact, adapt and perform tasks autonomously, increase the need for highly efficient, cross-platform software.

MLPerf Inference version 3.1 includes over 13,500 performance results from 26 providers, validating cross-platform benchmarking for AI workloads. The results spanned both the data center and edge devices, demonstrating the diversity of optimized deployments that are now being tested and shared.

Together, these signals show that market demand and incentives revolve around a common set of priorities, including maximizing performance per watt, ensuring portability, minimizing latency, and providing security and consistency at scale.

What needs to happen for successful simplification

To realize the promise of streamlined AI platforms, several things must happen:

Robust hardware/software co-design: Hardware features that are exposed in software frameworks (e.g., matrix multipliers and accelerator instructions) and, conversely, software designed to take advantage of the underlying hardware.

Consistent and 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 must collaborate. Common standards and projects help avoid reinventing the wheel for each new device or use case.

Abstractions that do not obscure performance: While high-level abstraction helps developers, they must still allow for fine-tuning or visibility when needed. The right balance between abstraction and control is key.

Security, privacy and trust built in: Especially as more computing shifts to devices (edge/mobile), issues such as data protection, secure execution, model safety, and privacy become important.

ARM as an example of ecosystem-led simplification

Simplifying AI at scale now relies on system-level design, where silicon, software, and developer tools evolve simultaneously. This approach enables AI workloads to run efficiently across diverse environments, from cloud inference clusters to battery-constrained edge devices. It also reduces the overhead of custom optimization, making it easier to bring new products to market faster. Arm (Nasdaq:Arm) is advancing this model by focusing on a platform that drives software and hardware improvements through the software stack. At COMPUTEX 2025, Arm demonstrated how its latest Arm9 CPUs, along with AI ISA extensions and Kleidi libraries, enable tighter integration with widely used frameworks such as PyTorch, ExecuTorch, ONNX Runtime, and MediaPipe. This alignment reduces the need for a custom kernel or manually tuned drivers, allowing developers to free up hardware performance without abandoning familiar tool chains.

The real-world implications are significant. In the data center, Arm-based platforms deliver improved performance per watt, which is critical for sustainably scaling AI workloads. On consumer devices, these improvements enable ultra-responsive user experiences and always-on, yet power-efficient, background intelligence.

More broadly, the industry is rallying around simplification as a design imperative, integrating AI support directly into hardware roadmaps, improving software portability, and standardizing support for mainstream AI runtimes. Arm’s approach demonstrates how deep integration across the computing stack can make scalable AI a practical reality.

Validate market and momentum

In 2025, nearly half of the computing shipped to major hyperscalers will run on Arm-based architectures, a milestone that underscores the 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 development represents a strategic pivot towards energy-efficient, scalable infrastructure optimized for the performance and requirements of modern AI.

At the edge, Arm-compatible inference engines enable real-time experiences, such as 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 provided native drivers for Arm Linux and Windows for GitHub Actions, simplifying 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?

Simplifying does not mean removing complexity entirely; It means managing them in ways that enable innovation. As the AI ​​stack stabilizes, the winners will be those that deliver seamless performance across a fragmented landscape.

From a future-facing perspective, expect:

Standards as guardrails: MLPerf + OSS combinations guide you where to improve next.

More upstream, fewer forks: Hardware features are in mainstream tools, not in custom branches.

Convergence of research and production: Faster delivery from paper to product via shared run times.

conclusion

The next phase of AI is not about exotic devices; It’s also about software that transitions well. When the same model efficiently reaches the cloud, client, and edge, teams ship faster and spend less time rebuilding the stack.

It is ecosystem-wide simplification, not brand-led logos, that will separate the winners. The practical rules of the game are clear: standardization of platforms, upstream improvements, and measurement using open standards. Discover how Arm AI software platforms are enabling this future – efficiently, securely, and at scale.

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2025-10-22 04:00:00