| AI Inference |
Real-time or batch model serving with strict latency, concurrency, and availability targets. |
1–4 GPUs for moderate traffic; 4–8 GPUs for high-throughput services. |
16–48 GB per GPU for compact models; 80 GB or more for large models and larger batch sizes. |
High-speed PCIe connectivity is usually sufficient; multi-GPU model serving may require a dedicated GPU interconnect. |
Redundant power supplies, front-to-back airflow, and sufficient thermal capacity for sustained inference loads. |
Critical |
Measured response latency, requests per second, GPU utilization, and uptime meet the service-level target. |
| Model Fine-Tuning |
Parameter-efficient tuning, supervised fine-tuning, and instruction tuning of medium or large language models. |
2–8 GPUs depending on model size, sequence length, batch size, and training method. |
24–80 GB per GPU; larger memory reduces the need for aggressive quantization or CPU offloading. |
High-bandwidth GPU-to-GPU communication is important for distributed fine-tuning and synchronized workloads. |
High-capacity power delivery, continuous-duty cooling, and fast local storage for datasets and checkpoints. |
Critical |
Training throughput, checkpoint time, loss convergence, and multi-GPU scaling efficiency are validated. |
| Large-Scale Model Training |
Distributed pre-training or continual training involving very large datasets and long-running jobs. |
8 or more GPUs per server, with multiple servers connected through a low-latency cluster fabric. |
80–192 GB per GPU is commonly targeted for large models, long context windows, and larger batch sizes. |
High-bandwidth GPU interconnects inside the server and low-latency networking between servers are essential. |
Data-center rack deployment, high-density power distribution, liquid or advanced air cooling, and environmental monitoring. |
Critical |
End-to-end tokens per second, scaling efficiency, job failure rate, and recovery time meet the project targets. |
| Computer Vision |
Image classification, object detection, segmentation, video analytics, and synthetic data generation. |
1–4 GPUs for development and inference; 4–8 GPUs for large training datasets or video workloads. |
16–48 GB per GPU for most projects; 80 GB or more may be useful for high-resolution images and large batches. |
Standard high-speed host connectivity is adequate for single-GPU jobs; multi-GPU training benefits from faster peer communication. |
Stable cooling for continuous training, fast dataset access, and optional video capture or transcoding support. |
High |
Images or frames processed per second, model accuracy, training time, and sustained utilization are measured. |
| Scientific Computing and HPC |
Numerical simulation, molecular modeling, computational fluid dynamics, weather modeling, and engineering analysis. |
2–8 GPUs for a workstation or node; larger clusters may use multiple GPU nodes. |
24–80 GB per GPU, depending on the working-set size and whether the application supports domain decomposition. |
Application-dependent; strong GPU peer-to-peer communication and low-latency cluster networking may be required. |
High reliability, error monitoring, redundant components, and cooling designed for uninterrupted computational workloads. |
High |
Floating-point performance, time to solution, parallel efficiency, numerical accuracy, and job stability are verified. |
| Generative AI and Media Processing |
Image generation, video generation, rendering, speech processing, 3D content creation, and visual effects. |
1–4 GPUs for individual workflows; 4–8 GPUs for production pipelines and concurrent users. |
24–80 GB per GPU for high-resolution assets, larger diffusion models, and complex rendering scenes. |
Fast GPU memory access and efficient peer communication improve multi-stage rendering and generation pipelines. |
High-airflow chassis, quiet operation where required, fast scratch storage, and adequate capacity for large media files. |
High |
Render time, frames per hour, generated assets per minute, visual quality, and workflow responsiveness are tested. |
| Virtual Workstations |
Remote engineering, CAD, design, simulation visualization, data science, and multiple concurrent GPU users. |
1–4 GPUs with workload partitioning or sharing capabilities based on user count and application requirements. |
16–48 GB per GPU for general users; 48 GB or more for advanced design and simulation applications. |
Reliable virtualization support, predictable GPU sharing, and low-latency network access are important. |
Rack or tower configuration based on deployment location, with strong manageability and moderate acoustic requirements. |
High |
Concurrent user count, application responsiveness, session stability, and resource isolation are validated. |
| Research and Development |
Rapid experimentation across machine learning frameworks, prototypes, benchmarks, and changing model architectures. |
1–4 GPUs initially, with modular expansion or access to additional cluster nodes as projects grow. |
24–80 GB per GPU to support a broad range of models and experimentation methods. |
Flexible support for standard and high-speed GPU communication to accommodate different frameworks. |
Accessible component layout, remote management, upgradeable memory and storage, and strong documentation. |
High |
Framework compatibility, experiment turnaround time, upgrade flexibility, and reproducibility are confirmed. |
| GPU Server Manufacturer Evaluation |
Assessment of a supplier's ability to deliver, integrate, support, and maintain the required GPU infrastructure. |
Configuration must match the validated GPU count, power budget, chassis capacity, and future expansion plan. |
Supplier should document usable memory, memory error protection, upgrade options, and workload-specific limits. |
Supplier should provide verified topology diagrams, network compatibility, cabling plans, and performance test results. |
Review power draw, cooling design, rack compatibility, remote management, firmware control, and service procedures. |
Critical |
Require a proof-of-concept test, written specifications, warranty terms, replacement times, and post-sales support commitments. |
Arkon Server
