Key application
AI Inference at Scale
Training, fine-tuning, and production inference on NVIDIA and AMD accelerated platforms - sized for the model, the token budget, and the rack you have.
Key application
AI Inference, Training, and Generative AI
This page covers the accelerated computing applications in the NTS catalog: deep learning training, production AI inference, large language model serving, generative AI, and computer vision. The same GPU platform families support all of them, but the right configuration depends on model size, batch behavior, and latency targets.
NTS specifies accelerator count and memory, PCIe topology or NVLink domain, host CPU and RAM ratios, NVMe scratch for dataset staging, and the network fabric that keeps GPUs fed. Systems are assembled, thermally validated under sustained load, and imaged with your framework stack in Fremont, CA before delivery.
Use this application hub to map workloads to NTS platforms—GPU servers, storage tiers, and rack integration—with Fremont staging and contract-ready quoting when required.
Each card links to deeper catalog or solution paths. Request an architecture review if you need CLIN structure, lead times, or a dual-vendor GPU comparison.
Workload path
From application intent to validated BOM
Application hubs connect mission use cases to engineer-to-order systems.
Start with the cards below to explore related platforms and capabilities. NTS sizes CPU, accelerator, memory, storage, and fabric together so power, cooling, and drivers match the workload—not orphan SKUs.
Federal and SLED buyers can carry the same architecture onto approved vehicles. Commercial teams get the same staging discipline without the GWAC paperwork when it is not required.
Workloads on this page
AI and accelerated computing applications
Each card maps a Key Application code from the product catalog to the NTS platforms that run it.
- 4-way to 8-way GPU nodes
- NVMe scratch for dataset staging
- Sustained-load thermal validation
- Right-sized accelerators per replica
- Throughput and latency targets
- Scale-out serving fleets
- Memory-per-GPU driven sizing
- High-bandwidth GPU interconnect
- Fabric for multi-node serving
- Diffusion and multimodal pipelines
- Fast asset and checkpoint storage
- Framework stack pre-imaged
- Multi-stream decode and inference
- Compact and short-depth options
- Fleet imaging before ship
Frequently asked questions
It depends on parameter count, precision, context length, and how many concurrent requests you serve. Give NTS the model or model class, target tokens per second or images per second, and latency ceiling, and we work backward to accelerator memory, GPU count per node, and node count.
- Model size and precision drive memory per GPU
- Concurrency and latency drive replica count
- Training jobs are sized on interconnect, not just GPUs
Usually not. Training rewards dense multi-GPU nodes with high-bandwidth GPU-to-GPU interconnect and fast scratch. Inference rewards throughput per watt, more independent replicas, and simpler PCIe topologies. NTS quotes them as separate tiers when that lowers total cost.
- Training: dense nodes, NVLink class interconnect
- Inference: efficient replicas, predictable latency
- Shared storage tier can serve both
That gets validated before the order. Accelerated nodes can draw well over 6 kW per chassis, so NTS reviews rack power, breaker capacity, inlet temperature, and airflow, then recommends air, rear-door heat exchanger, or direct liquid cooling to match the room.
- Per-rack power and thermal budget review
- Direct liquid and rear-door cooling available
- Airflow and containment guidance
Yes. NTS can pre-install the driver, CUDA or ROCm level, container runtime, and framework versions you standardize on, then verify GPU visibility, interconnect bandwidth, and thermals under sustained load before the system leaves Fremont.
- Driver, CUDA or ROCm, and container runtime imaged
- Interconnect bandwidth verified
- Burn-in report included
