AI For The Edge: Why Open-Weight Models Matter

The rapid advancements in AI have brought powerful large language models (LLMs) to the forefront. However, most high-performing models are massive, compute-heavy, and require cloud-based inference, making them impractical for edge computing.

The recent release of DeepSeek-R1 is an early, but unlikely to be the only, example of how open-weight AI models, combined with efficient distillation techniques, are changing the game for AI at the edge. Indeed, its root can be traced to Meta’s Tiny Llama, and Mistral’s 7B and Microsoft’s Phi-2 are of a similar ilk. The challenge is no longer just about creating powerful models—it’s about making AI efficient, scalable, and deployable across resource-constrained devices.

The shift toward AI at the edge

Traditional AI workloads have relied heavily on cloud-based inference, leveraging massive GPU clusters to process queries. While effective for large-scale applications, this approach presents challenges for edge computing:

• Latency Sensitivity: Edge applications like real-time monitoring, industrial automation, and smart IoT devices require instant decision-making.
• Bandwidth Constraints: Sending large amounts of data to the cloud for inference is costly and inefficient.
• Privacy & Security Concerns: Many AI applications—particularly in healthcare, finance, and consumer devices—benefit from on-device intelligence to reduce exposure to security risks.

For AI to truly thrive at the edge, we need models optimized for local processing, rather than relying solely on cloud resources.

The role of model distillation

But how models are being optimized is an important new nuance. For example, DeepSeek-R1 is not just another open-weight AI model—it represents a fundamental shift in AI optimization. DeepSeek-R1 leverages distillation, a widely used AI model compression technique that has been integral to optimizing neural networks for years.

Originally introduced to transfer knowledge from large models to smaller ones while retaining performance, distillation enables more efficient inference, reduced computational demands, and enhanced deployment flexibility. This process compresses the knowledge of large models into smaller, more efficient versions, making AI practical for real-world applications, especially in edge computing environments.

Distillation is an AI model compression technique where a smaller, student model learns from a larger, more powerful teacher model. In DeepSeek-R1, this process allows for:

• Smaller AI models (1.5B, 7B, 14B, 32B, 70B) that retain reasoning capabilities
• Optimized inference for low-power and resource-constrained environments
• A balance between accuracy and efficiency, making AI practical for embedded systems

By distilling DeepSeek-R1 into smaller versions, developers can leverage state-of-the-art AI performance on edge devices without requiring expensive hardware or cloud connectivity.

Why this matters for edge AI

For AI to succeed at the edge, it must be optimized for embedded computing environments. Here’s how the distillation approach directly addresses this:

• Lower Power Consumption: Smaller AI models can run efficiently on embedded MPUs, reducing power requirements for battery-powered devices.
• Reduced Compute Overhead: AI inference on platforms like Synaptics Astra MPUs becomes viable, ensuring real-time processing for smart devices, industrial applications, and autonomous systems.
• Scalability Across Industries: Whether it’s automotive, healthcare, or IoT, efficient AI opens doors for new use cases that weren’t feasible before.

The open source advantage

The first generation of LLMs were developed, perhaps necessarily, in a relatively closed fashion with large scale training as the critical objective. As we look to deploying AI in new ways, particularly to address the constraints and more user-specific inferencing needs of edge devices, an open-source approach facilitates a more personalized, efficient, and privacy-centric AI ecosystem for developing applications. Compared to the closed approach used in traditional LLMs, which prioritizes large-scale cloud deployment, open models allow for greater adaptability, security, and real-world usability, making them ideal for customized inferencing at the edge.

While DeepSeek has gotten most of the limelight, we are seeing the emergence of a new wave of open-weight models. Because their parameters are publicly available for use, modification, and deployment, open-weight models empower developers to use techniques like quantization and model pruning to create fast, privacy-centric AI solutions that offer efficiency, cost, and adaptability advantages compared to closed AI models.  On top of that, developers worldwide can contribute to improving the robustness and performance of models through open collaboration. Finally, organizations aren’t tied to proprietary AI ecosystems and can modify models to fit their specific needs.

Distilled models + AI-native compute: A new era for edge intelligence

While model distillation significantly reduces AI’s computational footprint, it must be paired with hardware designed for AI inference. At Synaptics, we’ve built the Astra platform as an AI-native compute solution, allowing optimized and open-source models to run efficiently at the edge.

This marks the beginning of a new era in AI deployment—where open-weight models, distillation techniques, and AI-native compute work together to deliver scalable, cost-effective intelligence at the edge.