Connect your software, model and deployment goals to a practical hardware plan. From one workstation to a shared GPU environment, define the workload before the parts.
Applications, model sizes, precision and concurrency.
GPU memory, host RAM, storage and network topology.
Representative tests and clear acceptance criteria.
Use your own words, then add a few practical details. See a preliminary direction and the assumptions behind it.
Use your own words. Application names, versions and the problem you want to solve are the best place to start.
NexTec builds workstations for local AI, engineering and CAD, content creation and other professional workflows. Start with the applications, project sizes and workspace requirements, then review a compatible complete-system proposal.
Plan training, fine-tuning, inference, retrieval, and private AI around your model and service requirements. The same GPU count can produce very different results across applications.
Engineering solvers and research applications need the right precision, memory bandwidth, and interconnect. Compare supported platforms using your actual code and dataset.
Scope systems for 3D rendering, digital twins, engineering visualization, and GPU-accelerated data analytics.
Build a deployment plan for GPU service offerings, enterprise capacity, or a research cluster. Think through provisioning and facility readiness before the first rack arrives.
For local AI, identify the model, quantization, serving engine and access controls. For engineering or rendering, specify the application version, plug-ins and any certification requirements.
