Exciting developments such as DeepSeek’s R1 announcement are extending opportunities to run large language models (LLMs) on edge devices. These advancements could have profound implications for edge computing, particularly in the realms of AIOps (artificial intelligence for IT operations) and observability. By enabling real-time insights and faster automations at the edge, enterprises can enhance their operational posture, drive down costs, and improve operational efficiency and resilience.

The Impact On Edge Computing

Edge computing has been gaining traction to process data closer to its source, reducing latency and bandwidth usage. Edge computing technologies help firms anticipate customer needs, act on their behalf, and operate businesses efficiently in localized contexts including internet-of-things-enabled scenarios. Running LLMs on laptops and edge devices enhances these benefits by delivering powerful AI capabilities right at the edge.

Training these models is a considerable challenge, something synthetic data could play a role in for AIOps, which is an approach that DeepSeek appears to have leveraged. DeepSeek-R1 claims to be as good if not better than other top-tier models, but it also offers unique advantages such as the ability to explain its answers by default. This transparency is crucial for building trust and understanding in AI-driven decisions in AIOps solutions.

Processing and analyzing vast amounts of data in real time at the edge enables more responsive and intelligent edge devices. This capability is particularly valuable in scenarios when immediate decision-making is critical but connectivity to a central source or cloud resources is intermittent and unreliable. Alternative considerations are the high costs for networking and risks associated with data traveling from the edge to the cloud and data center. Some AIOps strategic objectives are to improve prediction accuracy, enhance user experiences, and produce far-reaching contextual insights for IT operations; all these stand to benefit from LLMs processing telemetry at the edge.

Enhancing AIOps And Observability

AIOps and observability are crucial components of modern IT operations, providing the tools needed to monitor, analyze, and optimize complex systems. Observability tools capture real-time data points, including metrics, events, logs, and traces (MELT), which are essential for understanding system behavior and performance. AIOps leverages this data to reduce alert noise, troubleshoot issues, automate remediation, and provide deep, contextual real-time insights.

With LLMs running on edge devices, AIOps and observability can achieve new levels of real-time insight and automation. For instance, LLMs can analyze MELT data on the fly, identifying patterns and anomalies that might indicate potential issues, security or operational. The immediate analysis allows for quicker detection and resolution of problems, minimizing downtime and enhancing system reliability especially in environments with unreliable or irregular connectivity. The integration of smaller-footprint LLMs that can run at the edge, such as DeepSeek-R1, with AIOps can also lead to more proactive and predictive maintenance of devices and infrastructure or injection of risk-mitigating actions with no human intervention.

A New Paradigm For IT Operations

The integration of LLMs with edge computing and AIOps and observability represents a new paradigm for IT operations. It could be a game-changer for edge computing, AIOps, and observability if the advances of DeepSeek and others that are sure to surface run their course. This approach enables enterprises to harness the full potential of AI at the edge, driving faster and more informed decision-making. It also allows for a more agile and resilient IT infrastructure, capable of adapting to changing conditions and demands.

As enterprises embrace this new paradigm, they must rethink their data center and cloud strategies. The focus will shift to a hybrid and distributed model, dynamically allocating AI workloads between edge devices, data centers, and cloud environments. This flexibility will optimize resources, reduce costs, and enhance IT capabilities, transforming data center and cloud strategies into a more distributed and agile landscape. At the center will remain observability and AIOps platforms, with the mandate for data-driven automation, autoremediation, and broad contextual insights that span the entire IT estate.

Join The Conversation

Register for the upcoming webinar on February 12, The Importance Of AI-Driven IT Operations And AIOps In Edge, IoT, And OT Computing. During this webinar, I will be speaking with my colleague Michele Pelino about these very topics that DeepSeek has further catapulted into the news. As always, I invite you to reach out through social media to any of us if you want to provide general feedback. If you prefer more formal or private discussions, email inquiry@forrester.com to set up a meeting! You can also follow our research at Forrester.com by clicking on any of our names below.

Click the names to follow our research at Forrester.com: Carlos Casanova, Michele Pelino, and Michele Goetz.