AI Inference Cluster
High-availability infrastructure for serving AI models to production at scale.
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, so request a quote for a tailored build.
MIG where applicable for utilization
Overview
The AI Inference Cluster is engineered for serving models reliably to many concurrent users. Where a training cluster optimizes for raw throughput, an inference cluster optimizes for latency, availability, and cost-per-request. Nexus Compute designs and sources the right balance of GPUs, networking, and redundancy for your serving workload. Inference sizing works backwards from the response you have promised. Half-precision weights need roughly 2GB per billion parameters before any KV cache, and it is the cache that grows with concurrency and context length, so the memory question is usually about how many simultaneous requests you must hold, not about the model alone. Once each replica fits within a node the cluster scales horizontally and the interconnect matters far less than it does in training; where a model does not fit, it is split across GPUs and the intra-node link becomes a latency term in every request. Redundancy is the other axis: serving to customers means holding enough capacity for a node to fail or be patched without breaching the latency target, so the cluster is sized above peak rather than at it.
Who This Solution Is For
Business Benefits
Built for availability
Redundant nodes and load balancing keep AI services online even when individual components fail.
Optimized for latency
We specify GPUs and networking around your response-time targets, not just raw throughput.
Efficient cost-per-request
Right-sized GPUs and partitioning keep per-request economics competitive at volume.
Data and model control
Serving on owned infrastructure keeps customer data and proprietary models in your environment.
Typical Business Use Cases
Production serving of LLMs and generative models
High-availability internal AI services
Latency-sensitive real-time inference
Cost-optimized inference at high request volume
Serving with a committed latency target and failover headroom
Multi-tenant serving where GPU partitioning improves utilization
Industry Applications
Technical Overview
A multi-node serving cluster of GPU servers (H100, RTX PRO, or mixed) behind a load-balanced network, with redundancy for high availability and inference-serving software pre-configured. Designed around your latency, availability, and volume targets. An inference cluster is sized against three numbers you supply: peak concurrent requests, the token throughput each request needs, and the latency you have committed to. Those set replica count, the memory each replica needs for weights plus KV cache, and how much headroom is carried for failure and patching. Because replicas are independent, high-speed Ethernet with load balancing is usually sufficient and RDMA fabrics are reserved for models split across nodes: both are under /products/networking. The storage profile looks nothing like a training tier: the demand is fast cold starts and model-catalogue reads rather than sustained streaming, so local NVMe holding weights and container images matters more than aggregate capacity, with the tiering options under /products/storage. Host CPU and system memory are sized for tokenization, request handling, and batching, which are more CPU-bound than training pipelines.
| Compute Nodes | Multiple GPU servers (H100 / RTX PRO, configurable) |
| Availability | Redundant nodes + load balancing |
| Networking | Load-balanced 100GbE fabric |
| Serving Software | vLLM / Triton Inference Server pre-configured |
| GPU Partitioning | MIG where applicable for utilization |
| Monitoring | Latency, throughput, and health monitoring |
| Deployment | Single-site or multi-site (configurable) |
| MIG Profiles (H100 80GB) | Up to 7 isolated instances per GPU (e.g. 7x 1g.10gb, 3x 2g.20gb, or 1x 7g.80gb) |
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: genuine product, backed by the full manufacturer warranty.
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
How is an inference cluster different from a training cluster?
Inference clusters optimize for low latency, high availability, and cost-per-request; training clusters optimize for raw multi-node throughput. The hardware balance and networking differ accordingly.
Can you design for multi-site high availability?
Yes. We can specify infrastructure across two sites for resilience and advise on the trade-offs.
Which serving framework do you configure?
Commonly vLLM or NVIDIA Triton, but we configure to your stack and standards.
How do we size it if we do not know our traffic yet?
Start from a bounded estimate rather than a precise one: expected peak concurrent requests, average and maximum context length, and the latency you would consider a failure. Those three give a defensible starting configuration and, more usefully, tell you which direction to grow in. A cluster designed to add replicas is far easier to correct than one sized exactly to a forecast that turns out wrong.
What lead time should we expect?
Inference configurations often have more GPU options than training builds, which usually makes them easier to schedule: where a data-center part is on allocation, a different accelerator can frequently deliver the same serving capacity sooner. We give the current position for each option at quote so the choice between performance and delivery date is yours to make explicitly.
What power and cooling does an inference cluster need?
Often less per node than a training cluster, because serving configurations can use lower-power accelerators spread across more, lighter nodes. That flexibility is genuinely useful where a facility has a hard per-rack limit, since the same serving capacity can frequently be delivered at a density the building will accept. We work from your per-rack power and cooling ceiling and specify within it.
Hardware Assistance
Configure the AI Inference Cluster 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.