AI hardware planning starts with the workload: what the application must process, how quickly users need a result and how many requests may arrive together. A document-classification job, an interactive ERP assistant and a local vision application can have different capacity and deployment needs.
Separate training from inference, and distinguish a hosted model API from a model you operate yourself. This article offers a planning framework; it does not state tested hardware requirements for GreatWork or prescribe a particular processor, accelerator or server configuration.
Describe the workload in measurable terms
Record typical and peak request volumes, input size, expected output length, simultaneous users and acceptable waiting time. Include document processing, retrieval and ERP lookups in the application diagram. Those steps may consume time even when model generation is fast.
Define different targets for interactive and background work. A nightly classification queue may tolerate delayed completion, while a customer-service user needs a responsive screen. Keep the business acceptance criteria visible when comparing hosting options.
Budget memory beyond model weights
For a model you host, parameter count is only one part of memory planning. NVIDIA’s NIM memory guide explains that inference also needs memory for items such as the key-value cache, activations and runtime buffers. Exact requirements depend on the model, precision and serving configuration.
Test with the intended context lengths and concurrency. Reducing numerical precision or changing a serving configuration may alter capacity, compatibility or output quality, so verify the complete combination. Include host memory, storage, networking, monitoring and recovery arrangements in the operating plan.
"In the past, we managed software; today, we design autonomous processes that AI executes for us. The future belongs to those who automate not just the tasks, but the decision-making itself."
Benchmark a representative application
Use sanitized examples that resemble real orders, tickets or documents. Record the model and software versions, hardware configuration, request mix and test duration. Test both ordinary demand and the expected peak, including a restart or temporary downstream outage.
NVIDIA’s inference benchmarking guide distinguishes model performance measurement from application load testing. Track response time, time to first output, completed requests and resource use, while evaluating answer quality separately. A high generation rate does not establish that the business task was completed correctly.
Compare the complete operating cost
Compare hosted API, managed infrastructure and self-operated options using the same workload and quality criteria. Account for utilization, support effort, upgrades, availability and the cost of unused capacity. Include data-location and access requirements in the deployment decision.
Start with a measured pilot and expand capacity when its evidence supports the change. GreatWork’s artificial intelligence and cloud ERP pages provide relevant starting points for discussing the application and business-system architecture before selecting infrastructure.
I look forward to seeing how these developments will improve service levels and customer satisfaction in the freight industry!