AI Training
NVIDIA B200 Class
Dense accelerator platforms for advanced AI workloads
GPU densityMulti-GPU
Host CPUAMD EPYC class
MemoryLarge ECC configs
NetworkHigh-speed fabric options
AvailabilityProject based
Talk to engineeringAccelerated compute
AI & GPU Servers
Dedicated GPU infrastructure for AI inference, model training, rendering, simulation, and compute workloads where accelerator performance matters.
GPU infrastructure
Dedicated accelerators. No shared GPU.
From single-GPU servers to multi-node clusters, COREHOP designs GPU infrastructure around memory, interconnect, storage, and network requirements.
Inference
NVIDIA H200 Class
Large-memory GPU compute for inference and model workloads
GPU density1 to multi-GPU
StorageHigh-capacity NVMe
NetworkingHigh bandwidth
DeploymentDedicated bare metal
Build typeDedicated
Request designVisualization
Professional GPU
GPU servers for rendering, media, VDI, and accelerated applications
GPUWorkstation / data center
CPURyzen / EPYC class
StorageNVMe
Network10G+ options
ConfigurationFlexible
Request buildCluster design
GPU performance depends on the system around it.
COREHOP treats GPU projects as infrastructure designs, not a list of accelerator cards.
GPU Memory
Match accelerator memory and density to model size and concurrency.
CPU & RAM
Right-size host compute and memory for preprocessing, orchestration, and data pipelines.
Local NVMe
High-throughput storage for datasets, checkpoints, and fast scratch workloads.
Network Fabric
Scale-out networking for multi-node training and distributed applications.
AI infrastructure
Built for more than benchmarks.
Real GPU deployments need power, thermals, network architecture, storage throughput, and operational planning. COREHOP starts with the workload and works backward.
Inference and model-serving clusters
Training and fine-tuning environments
Rendering and media processing
Private AI infrastructure
“GPU infrastructure is a systems problem. The accelerator is only one part of the design.”
Planning a GPU deployment?
Send your GPU count, model requirements, power profile, storage needs, network topology, and target timeline.