On-premises vs. edge AI implementations for AI inference

AI inference at the edge vs. on-premises

Although inference at the edge and on-premises shares similar decision-making purposes, there are important distinctions between the two approaches in terms of scale, power consumption, performance, location and infrastructure demands. Some of the most important differences include the following:

• Location. On-premises AI inference is conducted within an organization's data center. A third party, such as OpenAI, Anthropic or Google, might host some components of on-premises inference, such as the LLM. Edge AI inference is conducted as close as possible to the device generating real-time data, such as robots, manufacturing equipment or autonomous devices.
• Workload types. On-premises AI inference uses machine learning models and can deliver significant computing power to handle high-volume enterprise-grade workloads and massive data pipelines. Computing involves multi-core CPUs, powerful GPUs and specialized AI accelerators such as tensor processing units. Edge AI inference depends on optimized models using far lighter computing hardware, such as system-on-chip (SoC) CPUs and NPUs.
• Latency. On-premises AI inference delivers low latency, but the latency can range from 10 to 50 milliseconds (ms), depending on network and centralized compute performance. This might be too long for real-time decision-making, such as for autonomous vehicles on busy city streets. Edge AI inference can deliver much shorter latency, as low as 1 ms, at the data source.
• Networking. On-premises AI inference relies on high-bandwidth, low-latency enterprise-grade network connectivity. Edge AI inference can operate and make decisions with no network connectivity, but can use broader networks when connectivity is available.
• Management. Experienced IT staff and developers manage on-premises AI inference platforms, often incorporating regular server maintenance cycles. Edge AI inference platforms, such as embodied AI robots, require ad hoc or connectivity-dependent maintenance windows, which pose complex security and management challenges for large, remote device fleets.

Considerations for on-premises vs. edge AI inference

Selecting the right deployment approach for AI inference is a critical decision in any AI project. Business, technology and regulatory leaders must collaborate and consider the following issues:

• Model complexity and limitations. On-premises AI inference enables a data center's infrastructure to perform inference processing tasks using complete, fully trained models. Network disruptions and latency can slow response times, so the inference task should be less time-sensitive. Edge AI inference is more limited, performing specific inference tasks with limited hardware capabilities, such as a single SoC processor, and relying on limited battery power. This requires a greatly simplified and optimized model that can return basic responses with ultra-low latency. This makes edge AI inference suited to embodied AI devices where fundamental real-time decision-making is critical.
• Connectivity and latency. On-premises AI inference relies heavily on networking connectivity to exchange data with storage systems, users, sensors and other data sources. The time needed to pass data to the on-premises AI, perform sophisticated inference and return a reply or decision reflects the combined latency of network communication, data access and processing time. The vulnerability of on-premises connectivity is that networking disruptions can cause significant latency or failed requests. This isn't intolerable for everyday business tasks, but it can be unacceptable for real-world interactions. Edge AI inference eliminates connectivity concerns by processing inference at the data source. Inference capabilities are limited because access to broader data sources can be limited, but edge latency can be virtually zero, which is ideal for dynamic real-world tasks.
• System resilience. System resilience for AI inference involves implementing fault-tolerant architecture across the infrastructure, careful model routing and performance telemetry to ensure predictable latency and uptime under burst or fault conditions. Such goals are possible in on-premises AI inference, where businesses can deploy hardware, power, cooling and supporting services. However, such elements aren't supported in edge AI inference, where processing, storage, power and connectivity are limited to ensuring graceful failure, which can prevent damage to the real-world environment if a fault or exception occurs at the edge.
• Data management. On-premises AI inference is well-suited to data management tasks such as storage, retention, deletion, security, privacy and meeting emerging data sovereignty demands. Edge AI inference has more limited data management capabilities due to physical space, hardware and power. The principal advantage of edge AI inference is that edge devices maintain their own data security and sovereignty because processing occurs on the edge platform itself rather than a central processing location. This limits the potential for data snooping and malicious access.
• Physical and system security. On-premises AI inference shares the physical and system security attributes of traditional data center infrastructure, including locked server rooms, identity and access management services and encrypted data storage. Edge AI inference platforms can vary in their security characteristics. Remote access is infrequent and restricted, but tight access controls are required to prevent external malicious attacks. Physical security can also vary. Small drones can be physically caught, while autonomous manufacturing systems can be immovable.
• Scalability. On-premises AI inference demands scalable infrastructure, extensive monitoring and highly automated, well-orchestrated resource management. Edge AI inference platforms don't offer significant scalability and are fixed to the amount of resources deployed with the platform. The need for power and space, along with heat and cooling limitations at the edge, can prevent meaningful scalability.

Choosing between on-premises and edge AI inference

Both on-premises and edge AI inference serve distinct goals and use fundamentally different criteria. By choosing the right option to support specific AI use cases or environments, business leaders can optimize AI inference implementation.

On-premises AI inference

On-premises AI inference uses dedicated data center resources and is best for steady, high-volume use where detailed computing supports detailed models and extensive data. Centralized data centers can be a better choice when data must remain on a local network and adhere to strict data security, governance and retention obligations.

Data center costs can be high, both in upfront investment and in the ongoing power, cooling and maintenance involved, but they can be predictable over the long term. However, procurement can be slow, requiring careful budgeting and months to scale to meet evolving AI usage.

Edge AI inference

Edge AI inference runs on remote hardware platforms located with or near the data source and is usually best for ultra-low-latency results critical for real-time decision-making. Edge AI is also preferred when connectivity to the internet or the central network is slow or unreliable. However, the model's complexity is reduced, relying on far lighter computing and storage resources, leaving edge AI best suited for tasks like object detection or image recognition.

Edge AI costs can be low to start when only a limited number of remote devices are involved, but they can grow with the number and sophistication of remote devices. Further, edge system management and updates can be challenging due to limited connectivity and poor bandwidth. This can affect compliance and liability when AI is involved.

Hybrid inference options

Inference is not a mutually exclusive decision. AI platforms can readily mix on-premises and edge systems to accommodate a wide range of situations and practical requirements. This puts a heavy focus on AI system architecture and design, often requiring skilled engineers to design, deploy and maintain a complex, mixed AI environment. An AI system might rely on edge AI inference for real-time tasks and use connectivity such as 5G to exchange data and make more sophisticated decisions or produce custom content at a centralized on-premises AI inference data center.

Stephen J. Bigelow, senior technology editor at TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.