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Modern manufacturers are exploring and deploying AI across use cases such as predictive maintenance, quality control, supply chain optimization, digital twins and operational efficiency. Yet access to AI models alone is not enough to deliver scalable business outcomes.
As manufacturers modernize operations, the ability to securely manage and analyze growing volumes of operational and production data has become increasingly important. Concerns around operational control, latency, connectivity and unpredictable consumption costs are driving greater interest in controlled deployment models. In fact, 79% of enterprises have already moved AI workloads outside the public cloud
At the same time, AI workloads are becoming increasingly sophisticated, spanning real-time production monitoring, advanced analytics, large language models, autonomous systems and emerging agent-based applications. As adoption accelerates, manufacturing leaders are placing greater emphasis on efficiency, resilience, performance and scalability.
Success requires more than simply adding compute capacity. It demands an AI strategy aligned with business objectives, operational requirements and evolving AI workloads. Organizations that build the right foundation are better positioned to optimize production, improve product quality, reduce downtime and drive innovation across the manufacturing enterprise.
Different use cases, different needs
AI use cases for manufacturing can deliver significant business value, but each places different demands on infrastructure. And most manufacturers are running multiple AI workloads simultaneously, each with unique performance, reliability and cost requirements. As a result, AI stack sizing becomes a multidimensional challenge that extends across compute, storage and networking.
For example, some applications can run efficiently on CPUs, while others require GPU acceleration. Model training and analytics workloads often depend on high-throughput storage, whereas real-time inference requires low-latency access to data. Distributed factory environments also rely on secure, high-performance connectivity.
Right-sized infrastructure provides the performance, scalability and cost efficiency needed to support AI innovation across modern manufacturing operations.
Inference is often where AI delivers the greatest business impact at scale. Unlike periodic training workloads, inference powers continuous, real-time decision-making, supporting applications such as predictive maintenance, quality control and operational optimization. As usage grows, infrastructure inefficiencies can quickly affect both performance and operational costs.
In all cases, infrastructure that is undersized can create processing bottlenecks and impede critical business processes, while overprovisioned environments increase costs unnecessarily.
AI for manufacturing leads to real results
- 3.5% reduction in downtime using Dell predictive maintenance solutions
- 13% lower costs for maintenance using Dell AI and edge solutions
A practical framework for sizing AI infrastructure
Successful AI deployments in manufacturing depend on balancing five key factors.
#1 Model size
Model size is measured in parameters.
With models ranging from smaller deployments:
(1–7 billion) to frontier-scale models (1+ trillion).
Larger models demand more compute, memory and specialized infrastructure. Selecting the right model size for the use case is critical to controlling costs while meeting operational requirements.
#2 Precision
Precision directly affects compute and memory requirements.
- Higher-precision formats such as: FP32 and FP16/BF16 can improve model accuracy but require more resources.
- Lower-precision options, including: FP8 and Int8 are often well suited for inference workloads and can help reduce compute demands, memory consumption and operational costs.
#3 Tokens and context length
Determines how much information a model can process at one time.
Longer-context windows require additional memory and KV cache capacity.
Which becomes increasingly important when analyzing production data, maintenance records, supply chain information or complex operational data sets.
#4 Latency
Influences how quickly AI-generated results are delivered.
Low and consistent latency is essential for many manufacturing applications, particularly those supporting real-time monitoring, quality control and operational decision-making.
Metrics such as time to first token (TTFT) and inter-token latency (ITL) help measure responsiveness and should align with operational requirements.
#5 Input/output sequence length (ISL/OSL)
Varies by application.
Different workload patterns, such as predictive analytics, conversational AI, document analysis or complex reasoning, affect memory utilization, cache requirements and throughput.
Understanding these characteristics early helps avoid costly infrastructure mismatches and aligns infrastructure with workload demands.
Understanding how these elements interact is essential for achieving the right mix of performance, scalability, reliability and cost efficiency.
Dell AI Factory with NVIDIA for manufacturers
Manufacturing AI workloads are becoming increasingly complex, data-intensive and distributed. Optimization cannot be achieved through individual technology components alone. It requires a coordinated approach across compute, networking, storage, software and automation.
The Dell AI Factory with NVIDIA is a modular, validated, full-stack platform designed to help manufacturers move AI initiatives from pilot to production. By bringing together every layer of the AI infrastructure stack, it provides a unified foundation for deploying and scaling AI across the enterprise. The result is a more streamlined approach to AI adoption that helps simplify operations, improve efficiency, accelerate innovation and support better business outcomes while maximizing return on investment.
Accelerated compute, the engine of AI optimization
Servers provide the compute foundation for AI, shaping performance through processing power, GPU availability, memory resources and the ability to scale as workloads grow. The objective is to deploy infrastructure that matches the needs of each use case while avoiding unnecessary costs or performance constraints.
This flexibility is important because manufacturing AI workloads can vary significantly across production, quality, supply chain and engineering functions. With a broad server portfolio, you can align infrastructure investments with specific operational goals and scale confidently as requirements evolve.
The Dell PowerEdge portfolio supports a broad range of manufacturing AI initiatives, including:
- Large-scale training and agentic AI: PowerEdge XE9780 and XE9785 with NVIDIA HGX™ B300 GPUs deliver the performance required for complex, data-intensive workloads.
- Model training and fine-tuning: The PowerEdge XE9780 provides a scalable environment for developing, adapting and refining AI models.
- Generative AI and inference: PowerEdge R770 and R7725 with PCIe NVIDIA GPUs, including NVIDIA RTX PRO™ 6000 or 4500 Blackwell Server Edition, support a wide spectrum of everyday AI applications.
- Inference optimization: The PowerEdge XE7745 combines performance, efficiency and low latency with NVIDIA RTX PRO™ 6000 Blackwell Server Edition GPU configurations.
- Distributed edge environments: PowerEdge XR-Series systems are built for factory floors, production facilities, warehouses and other operational settings where durability, space efficiency and performance are critical.
Enable AI performance with the right networking foundation
AI success depends on moving data quickly and efficiently across infrastructure. Networking provides the foundation for connecting GPUs, servers and storage, helping AI workloads perform at scale. As manufacturers expand AI initiatives, network architecture plays an increasingly important role in supporting responsiveness, throughput and operational efficiency. The Dell PowerSwitch SN6000-Series with NVIDIA Spectrum-6 supports a wide range of AI requirements:
Large-scale and back-end AI: Provides the high-speed connectivity required for demanding AI workloads.
- Massive scale-out: Delivers up to 409.6 Tb/s switching capacity, 800 Gb/s per port and ultra-low latency for large AI environments.
- Efficiency and reliability: Liquid cooling and co-packaged optics help improve energy efficiency, thermal performance and system uptime.
- Integrated infrastructure: Works with Dell rack-scale solutions to bring together networking, power and cooling across AI deployments.
- Open operations: Support for SONiC and Dell Fabric Manager helps simplify automation, operations and lifecycle management.
Create a scalable data foundation for AI
AI workloads are only as effective as the data that supports them. Whether you’re training models, running inference or analyzing operational data, storage plays a critical role in delivering the speed and accessibility AI requires.
The Dell AI Data Platform with NVIDIA provides the data foundation for the Dell AI Factory with NVIDIA, bringing together Dell’s AI-optimized storage and modular data engines with NVIDIA accelerated computing, networking, and NVIDIA AI Enterprise software. Designed for the agentic AI era, it helps enterprises turn distributed, multimodal data into AI-ready data for RAG, search, analytics, agentic workflows, and large-scale data processing—while providing the performance, security, governance, and scalability required for production AI.
Dell offers the following storage engines from the Dell AI Data Platform:
- PowerScale and ObjectScale to support the complete data lifecycle, from ingestion throughlong-term retention, across a variety of manufacturing AI workloads.
- Dell Lightning File System, designed for high-performance AI environments where accelerating model training and maximizing GPU efficiency are top priorities.
By aligning storage capabilities with workload demands, you can improve resource utilization, maintain consistent performance and create a foundation that supports AI growth over time.
Simplify AI deployment and management
The ability to scale AI depends not only on infrastructure but also on how efficiently it can be deployed, managed and maintained. Dell Automation Platform provides a unified orchestration layer to help you:
- Deploy and manage validated blueprints for consistent outcomes.
- Automate manual tasks to shorten average deployment time by roughly one week
- Establish greater consistency across AI deployments.
- Align compute, storage and networking resources with workload requirements.
The result is a more streamlined, repeatable operating model that reduces manual effort and helps accelerate the transition from pilot projects to production environments.
Many AI initiatives deliver promising results in testing, but what works in a single production line or facility may not easily extend across operations, sites or use cases. Validated reference architectures help overcome these challenges by combining compute, storage, networking and software into proven designs that simplify deployment and reduce risk.
Software: the control layer that turns infrastructure into an AI platform
Software and automation are what make the Dell AI Factory with NVIDIA operate as a unified system rather than a collection of separate components. At a high level, the software layer helps automate deployment, centralize lifecycle management, and keep AI workloads consistent across core, edge and distributed environments.
This reduces operational complexity and helps teams move AI workloads from pilot to production faster, with less manual effort and more repeatability.
NVIDIA AI Enterprise brings together microservices, frameworks and libraries for AI development with advanced GPU orchestration and infrastructure management in a fully supported, production-ready, commercial software suite.
Deploy leading open source tools and AI models with confidence, improve productivity and time to value, and run AI workloads at scale with optimized resource utilization.
Build for the workload
Manufacturers achieving the greatest success with AI typically follow a focused approach:
- Define your AI requirements. Establish model size, precision, latency and context needs.
- Deploy AI where it delivers the most value. Balance performance, operational control, security and cost.
- Match infrastructure to the workload. Ensure resources align with business and operational demands.
- Take a full-stack approach. Integrate compute, storage, networking and software to work as a unified system.
- Prepare for future growth. Leverage validated reference architectures to scale more efficiently and with greater confidence.
Speeding pilot to production timelines
The Dell AI Factory with NVIDIA brings these capabilities together in a modular platform, helping manufacturers accelerate AI adoption, improve operational efficiency and scale innovation with confidence.
Moving from proof of concept to production is where many AI efforts stall. What works for a single team doesn’t easily scale across environments, users or use cases. Validated reference architectures help bridge that gap by combining compute, storage, networking and software into tested, repeatable designs that reduce risk and speed deployment.
Take the next step
No two AI environments are exactly alike. Taking a tailored approach can help you simplify complexity, improve operational efficiency and support long-term growth across a variety of manufacturing use cases. As you refine your AI strategy, consider how a modular, validated platform can provide the flexibility needed to meet evolving production and business objectives.
To get started:
- Evaluate your current infrastructure to identify opportunities for optimization and right-sizing.
- Review reference architectures for use cases such as predictive maintenance, quality inspection, supply chain optimization and digital twins.
- Engage with a Dell AI specialist to develop a practical road map for scaling AI from initial projects to production deployment.