AI Factory / GPU Compute
Design and integration of OEM servers based on NVIDIA HGX B300, DGX and upcoming Rubin platforms, sized for training, fine-tuning, RAG, agents and inference.
GPU • NVLink • NVSwitch • CUDA
AI Infrastructure Integrator
Infrastructure that turns GPUs into AI in production.
NGG Tech integrates accelerated computing, networking, storage and software for high-performance AI environments: from HGX B300 and Vera Rubin to Kubernetes + vLLM clusters, GPU-powered VDI and fabrics of up to 800 Gb/s.
NVIDIA HGX B300Vera RubinKubernetesvLLMvGPU / VDISpectrum-X / InfiniBand
01 / Integration
Every layer, integrated.
Compute, NVLink, 800G networking, NVMe storage and Kubernetes designed together, as a single system rather than parts bought separately.
02 / Production
From GPU to token, at scale.
vLLM, autoscaling and observability turn GPU capacity into throughput, low latency and a predictable cost per token.
800GEthernet / InfiniBand fabrics for AI scale
SCROLL TO EXPLORE
ComputeBlackwell Ultra and Rubin
OrchestrationKubernetes + GPU Operator
ServingvLLM and inference APIs
VirtualizationVDI, vGPU and multi-user
Network FabricEthernet / InfiniBand up to 800G
From rack to token.
We design the complete environment (power, cooling, GPUs, networking, cluster, observability and serving) with a focus on throughput, low latency, availability and cost per token.
Kubernetes + vLLM
Inference clusters with GPU Operator, autoscaling, queues, observability and OpenAI-compatible APIs. vLLM for high throughput and efficient memory use.
K8s • vLLM • Gateway • AutoscalingGPU-powered VDI
GPU-accelerated virtual desktops and workstations for engineering, creative work, CAD, rendering, data science and professional workloads.
vGPU • VDI • GPU Sharing • WorkstationsHigh-Speed Networking
Low-latency, high-throughput fabrics with Spectrum-X Ethernet or InfiniBand, ConnectX SuperNICs, BlueField DPUs and spine-leaf design.
400G / 800G • RoCE • RDMA • DPUStorage for AI
NVMe and parallel storage architectures built for ingestion, datasets, checkpoints, embeddings and training and inference pipelines.
NVMe • Object • Parallel FS • GPUDirectObservability & SRE
Telemetry for GPUs, nodes, network, applications and models, with utilization, latency, error, consumption and capacity metrics for 24x7 operation.
DCGM • Prometheus • Grafana • OpenTelemetryToday and the next generation.
NGG Tech works with different server manufacturers and integrates the ecosystem around the NVIDIA platform, without mixing up HGX, DGX and rack-scale.
HGX B300 / Blackwell Ultra
ShippingFor high-density OEM servers: the HGX B300 platform combines 8 Blackwell Ultra SXM GPUs with fifth-generation NVLink. HGX is the platform/baseboard that OEM manufacturers integrate; DGX is NVIDIA's complete system.
8× B300Blackwell Ultra SXM
2.1 TBtotal GPU memory
14.4 TB/stotal NVLink
NVIDIA Vera Rubin
Production ramp 2026The new generation combines the Vera CPU, Rubin GPUs, sixth-generation NVLink and next-generation networking. It comes as HGX Vera Rubin NVL8 servers and as a rack/POD architecture for large-scale AI Factories.
HBM4next-generation memory
6th genNVIDIA NVLink
800Gnetworking and accelerated services
A network built for AI.
In an AI cluster, every GPU exchanges data with the others all the time. If the network congests, the GPU sits idle, waiting. With NVIDIA Spectrum-X, Spectrum-4 switches and BlueField-3 SuperNICs carry that traffic over Ethernet with RoCE, adaptive routing and congestion control.
NGG Tech designs the spine-leaf fabric, sizes the 400G/800G ports and delivers the network integrated with the cluster and Kubernetes.
51.2Tb/s of capacity per Spectrum-4 switch
800Gper Ethernet port
1.7×AI performance vs. traditional Ethernet, according to NVIDIA
● Clip 00:32 – 00:58 · looping
A stack designed to scale.
For AI in production, GPUs alone are not enough. Final performance depends on the whole: compute, interconnect, storage, orchestration, serving, governance and observability.
The design can start with a single HGX/DGX node and grow into multiple racks with Kubernetes, a GPU pool, vLLM, inference queues, high availability and a dedicated fabric.
Request a technical designApplications
RAGAgentsGenAI AppsVDI
Serving
vLLMAPI GatewayLoad BalancerModel Registry
Orchestration
KubernetesNVIDIA GPU OperatorAutoscalingScheduling
Observability
DCGMPrometheusGrafanaOpenTelemetry
Data
NVMeObject StorageVector DBParallel Storage
Network
Spectrum-XInfiniBandConnectXBlueField
Compute
HGX B300Vera RubinOEM ServersDGX
Engineering before purchasing.
The GPU choice comes after defining the workload, model, SLA, growth, available power, network and operating strategy.
01 / ASSESSMENT
Workload & capacity
Models, context, TPS, concurrent users, training, inference, VDI and projected growth.
02 / DESIGN
Complete architecture
Compute, power, cooling, networking, storage, HA, security and cluster software.
03 / DEPLOY
Implementation
Rack & stack, firmware, fabric, Kubernetes, drivers, GPU Operator, vLLM and observability.
04 / OPERATE
Continuous optimization
Benchmarks, capacity planning, cost per token, GPU efficiency, upgrades and operational support.
Let's size your next AI infrastructure.
Tell us about your current setup, number of users, the models you plan to run and the goal of the environment. NGG Tech lays out the architecture and the roadmap for growth.
On-premisesColocationPrivate AI CloudHybrid
NVIDIA and its product names are trademarks of their respective owners. NGG Tech acts as an independent integrator.