On-Prem AI Inference Guide | L40S Cluster Architecture

Day 0: 把需求翻译成 GPU 数量

每一次 on-prem 推理部署都始于同一个问题：跑什么模型、服务多少并发、延迟承诺多少？用这三问做规模测算：

The NVIDIA L40S (48GB, 1,466 TFLOPS FP8) is the 2026 workhorse for this envelope: a 2U node with two L40S cards serves a 70B-class model in production, and a 4-8 node cluster covers most enterprise RAG and copilot workloads. Day 0’s deliverable is a GPU count and a latency budget – nothing else.

Day 1: 节点与网络——L40S 的标准栖身之所

Build the inference node on proven 2U hardware with 350 W GPU power delivery:

• Node: a dual-socket 2U chassis with 2 x L40S (700 W total GPU TDP) – the PowerEdge R760 (2 x 350 W double-width slots) or xFusion 2288H V7 (4 x double-width for denser builds).
• Fabric: one E810-CQDA2 100GbE per node – tensor-parallel shards exchange activations over the fabric; 100GbE keeps multi-node latency predictable.
• Storage: NVMe for model weights and KV-cache checkpointing; a few hundred GB per model is enough at this scale.
• Management: iDRAC/iBMC telemetry for GPU thermals – a 700 W GPU load changes airflow requirements vs CPU-only nodes.

For training-class density, the 6U XE9680 (8-GPU) or 4U FusionServer G5500 V7 (10-GPU) scale the same stack upward – the L40S cluster starts at 2U and grows by adding nodes.

Day 2: 推理栈——让硬件跑满

The hardware is only half the deployment. The software stack decides real throughput:

• Runtime: vLLM or TensorRT-LLM with FP8 quantization – the L40S’s 1,466 TFLOPS FP8 is what makes small-cluster serving viable.
• Batching: continuous batching keeps GPU utilization high under mixed traffic; target 70-85% utilization at peak.
• Sharding: tensor parallelism across 2-4 GPUs per model replica; keep shard count within the node where possible.
• Multi-model: a shared GPU pool (Kubernetes with GPU scheduling) serves several small models on one cluster instead of one-model-per-node.

Measure tokens-per-second per GPU and first-token latency from Day 2 – those two numbers decide whether you add GPUs or tune batching.

Day 3: 上线、监控与扩缩容

Go-live is the start of operations, not the end:

1. Monitor: GPU utilization, KV-cache memory, token throughput, p95 first-token latency – per node and per model.
2. Alert: latch onto thermal (iDRAC/iBMC) and memory pressure before users notice degradation.
3. Scale: add L40S nodes to the pool as load grows; rebalance replicas via the scheduler, no downtime.
4. Review: monthly – model refresh (new quantizations), traffic shape changes, and whether FP8 or smaller models cut cost further.

The Cluster Template

Every node is identical (same chassis, same NIC, same stack) – the cluster scales by template replication, which is exactly how on-prem fleets stay operationally sane.

FAQ

• Why L40S over H100? For inference, FP8 density and 350W power make L40S far cheaper per token at enterprise scale.
• Two GPUs per node enough? For 7-70B models in production, yes – scale nodes out, not up.
• Need RDMA? For 2-4 GPU tensor parallelism, 100GbE TCP is sufficient; RDMA (iWARP/RoCE) helps above 8 GPUs.
• Storage requirements? NVMe for weights and KV-cache checkpoints; a few hundred GB per model.
• Where to start? Size by model+concurrency (Day 0), then order one pilot node and measure.

Ready to build your inference cluster? We deliver L40S-ready nodes (R760 / 2288H V7 with dual-L40S and 100GbE) as a complete stack – factory-direct pricing, 3-year warranty, global shipping and free cluster design consultation from our authorized partner team. Contact us for a Day-0 sizing call.

Deployment note: the same Day 0-3 sequence applies whether you standardize on R760, 2288H V7 or a G5500-scale node – the sizing math and the software stack are identical.