Nexus H200 32-GPU Inference Cluster with Parallel Storage
Low-latency 32-GPU H200 serving with a GPUDirect parallel storage backbone.
We help you choose, configure, and deliver the right system — no obligation.
Configuration at a Glance
Tailored per engagement. Full technical overview below.
Configuration Options
Core specifications for this system. Every component is configurable to your workload — request a quote for a tailored build.
32× NVIDIA H200 141GB HBM3e SXM5 (4× HGX H200 nodes)
Dual Intel Xeon or AMD EPYC per node
Up to 2TB DDR5 ECC per node
GPUDirect-certified parallel filesystem (WEKA/VAST-class)
Overview
This 32-GPU H200 cluster is tuned for high-availability, low-latency model serving, pairing four HGX H200 nodes with a GPUDirect-certified parallel storage tier for fast model and KV-cache loading. Nexus Compute specifies, integrates, and acceptance-tests compute, fabric, storage, and serving software as one warranty-backed system sourced through authorized channels.
Who This Solution Is For
Business Benefits
Built for low latency
We tune GPU count, fabric, and serving software around your response-time targets so concurrent users get fast, consistent inference.
Storage that keeps GPUs busy
A GPUDirect-certified parallel tier streams weights and KV-cache directly to GPU memory, slashing cold-start and model-swap time.
Highly available serving
Redundant nodes and load balancing keep services online through component failures, with 141GB H200 memory hosting large models per node.
Typical Business Use Cases
Production serving of large LLMs and generative models
High-concurrency, latency-sensitive inference APIs
Rapid model and adapter swapping at scale
Cost-optimized inference at high request volume
Industry Applications
Technical Overview
Four NVIDIA HGX H200 8-GPU SXM5 nodes (32× H200 141GB HBM3e) sit behind a load-balanced NDR InfiniBand and high-speed Ethernet front end, with NVSwitch all-to-all inside each node. A GPUDirect Storage-certified parallel filesystem (WEKA- or VAST-class) feeds weights and KV-cache, and vLLM or NVIDIA Triton serving is pre-configured to your latency and throughput targets.
| GPU / Accelerator | 32× NVIDIA H200 141GB HBM3e SXM5 (4× HGX H200 nodes) |
| GPU Interconnect | NVSwitch intra-node; NDR InfiniBand inter-node |
| CPU | Dual Intel Xeon or AMD EPYC per node |
| Memory | Up to 2TB DDR5 ECC per node |
| Storage | GPUDirect-certified parallel filesystem (WEKA/VAST-class) |
| Networking / Fabric | Load-balanced NDR InfiniBand + high-speed Ethernet front end |
| Serving Software | vLLM or NVIDIA Triton pre-configured |
| Management | Out-of-band BMC + latency/throughput monitoring |
| Warranty | Nexus-backed, NVIDIA AI Enterprise eligible |
Specifications are indicative and configured to each engagement. Request a quote for a configuration tailored to your requirements.
Warranty, Support & Fulfillment
Every system ships from an authorized channel, configured and tested, with the documentation enterprise buyers need — backed by warranty and a dedicated account team.
Enterprise Warranty
Full manufacturer warranty with optional on-site, next-business-day support and extended coverage.
Authorized Channel
Sourced through Tier-1 distribution and OEM partners — never grey market. Asset & warranty records included.
Lead Time & Deployment
48-hour quotes, then configured, burn-in tested, and delivered on a committed schedule.
Nationwide Fulfillment
Coordinated logistics, rack-and-stack, and delivery wherever your infrastructure lives.
Frequently Asked Questions
Why does storage matter for inference?
Serving large models means loading tens to hundreds of gigabytes of weights and managing KV-cache; a GPUDirect parallel tier streams data straight to GPU memory, cutting cold-starts and enabling fast model swaps that direct-attached disks cannot match.
How is this different from a training cluster?
It optimizes for latency, availability, and cost-per-request rather than raw throughput — the node balance, load-balanced front end, redundancy, and pre-configured serving stack are tuned for production traffic, not long training runs.
Can it scale as request volume grows?
Yes. Additional H200 nodes join the same fabric and load balancer, and the parallel storage tier scales independently, so capacity grows with demand without redesign.
Hardware Assistance
Configure the Nexus H200 32-GPU Inference Cluster with Parallel Storage with Nexus Compute
Tell us your requirements and a hardware specialist will help you specify, configure, and quote the right system — typically within two business days. No obligation.