Published: July 4, 2026 | Category: Product Spotlight | QSCompute
Building an edge AI server from components is slow, risky, and expensive when it goes wrong. Between GPU compatibility matrices, PCIe bifurcation quirks, storage layout decisions, and BIOS tuning, a scratch build can burn 2–3 engineering weeks — time your deployment schedule doesn't have. QSCompute's pre-configured edge AI servers ship as complete, burn-in tested systems: GPU installed, CUDA/cuDNN loaded, storage partitioned, networking configured. Plug in power and Ethernet, push your model, and go. This post covers our three standard configurations — from entry-level single-GPU node to multi-GPU production server — plus optional 边缘AI networking upgrades.
| Configuration | QS-Edge-Nano | QS-Edge-Pro | QS-Edge-Cluster |
|---|---|---|---|
| Use Case | 1–4 camera streams, lightweight models (YOLO, ResNet) | 8–16 camera streams, multi-model serving, fine-tuning | 16–64 camera streams, LLM inference, production AOI |
| GPU | 1× NVIDIA RTX 4000 Ada (20 GB) | 1× NVIDIA L40S (48 GB) | 2× NVIDIA L40S (96 GB total) |
| CPU | Intel Xeon E-2488 (8C/16T) | Intel Xeon 6 3508P (8C/16T) | Intel Xeon 6 5520+ (20C/40T) |
| RAM | 64 GB DDR5-5600 ECC | 128 GB DDR5-5600 ECC | 256 GB DDR5-5600 ECC |
| OS Storage | 1 TB NVMe Gen4 (industrial, 1 DWPD) | 2× 1 TB NVMe Gen4 (RAID 1) | 2× 2 TB NVMe Gen4 (RAID 1) + 4 TB U.2 data drive |
| Networking | 2× 10GbE SFP+ | 2× 25GbE SFP28 | 2× 25GbE SFP28 + 1× 100GbE QSFP28 |
| Power Supply | 850 W redundant | 1,200 W redundant | 2,000 W redundant (2+1) |
| Chassis | 1U rackmount, 28" depth | 1U rackmount, 28" depth | 2U rackmount, 30" depth |
| Operating Temp | 5–35°C | 5–35°C | 5–35°C |
| Price (USD) | $7,499 | $14,800 | $32,500 |
| Lead Time | In Stock — 3 days | In Stock — 5 days | In Stock — 7 days |
The QS-Edge-Nano is built for small-scale factory-floor inference: 1–4 camera streams running YOLOv8, ResNet-50 classifiers, or lightweight vision transformers. The RTX 4000 Ada's 20 GB VRAM handles batch sizes up to 32 for typical industrial models, and the Xeon E-2488's 8 cores manage frame ingest and pre-processing without a bottleneck.
Ideal for: Single-line defect detection, barcode/OCR verification, simple pick-and-place vision.
Expansion: 1 free PCIe Gen5 ×16 slot for a second GPU or 100GbE NIC. 2× M.2 slots for additional NVMe storage.
$7,499
The QS-Edge-Pro is our most popular configuration. The L40S GPU delivers 48 GB GDDR6 ECC and 91.6 FP16 TFLOPS — enough to run 3–4 production models simultaneously (e.g., defect detection + OCR + anomaly scoring) on 8–16 camera streams. The Xeon 6 3508P supports DDR5-5600 ECC and PCIe Gen5, with Intel DLB (Dynamic Load Balancer) accelerating packet distribution across cores for multi-stream ingest.
Ideal for: Multi-line AOI, multi-model inference serving, edge fine-tuning (LoRA/QLoRA on ≤13B models).
Storage: Dual 1 TB NVMe in RAID 1 for OS + model registry. Add optional 4–8 TB U.2 for inference log retention.
$14,800
When a single L40S isn't enough — 16–64 camera streams, LLM-based defect reasoning, or running NVIDIA Metropolis microservices — the QS-Edge-Cluster delivers dual L40S GPUs (96 GB VRAM total) with NVLink Bridge for direct GPU-to-GPU transfers. The Xeon 6 5520+ provides 20 cores and 40 threads for ingest, with Intel DLB and DSA (Data Streaming Accelerator) offloading DMA and memory copies.
Ideal for: High-throughput AOI (16+ lines), LLM-augmented quality inspection, edge training on mid-size datasets.
Networking: 100GbE QSFP28 uplink for aggregating edge cluster traffic to a central NAS or cloud bucket.
$32,500
Factory-floor edge AI generates massive data flows: camera streams at 1–4 Gbps each, model updates to/from cloud, and inference logs to centralized storage. QSCompute offers pre-configured networking bundles for each server tier:
| Networking Tier | Switch | Ports | Throughput | Management | Price |
|---|---|---|---|---|---|
| QS-Net-10G | MikroTik CRS312-4C+8X | 12× 10GbE SFP+ | 240 Gbps | RouterOS L5 | $599 |
| QS-Net-25G | Mellanox SN2410 | 48× 25GbE + 8× 100GbE | 4 Tbps | Cumulus Linux | $4,200 |
| QS-Net-100G | Mellanox SN2700 | 32× 100GbE QSFP28 | 6.4 Tbps | Cumulus Linux | $8,900 |
The counter-argument is simple: "I'll just buy the components and assemble them myself." Here's what you're actually paying for with a QSCompute pre-configured server:
| Activity | DIY Time | QSCompute |
|---|---|---|
| GPU compatibility research | 2–4 hours | Pre-validated matrix |
| PCIe bifurcation BIOS setup | 1–3 hours (trial and error) | Pre-configured and documented |
| CUDA / cuDNN / TensorRT install | 2–4 hours | Pre-loaded and tested |
| Storage partitioning & RAID setup | 1–2 hours | Pre-partitioned, RAID ready |
| 48-hour burn-in & thermal validation | 48 hours (clock time) | Done — with report |
| Warranty integration (buck-passing risk) | 5+ vendors to manage | Single point: QSCompute |
The DIY approach saves ~$1,500–2,500 on a $15K server — but costs 1–2 engineering weeks. At $100/hour fully loaded, that's $4,000–8,000 in labor. And if a component is DOA or incompatible, you're debugging a multi-vendor supply chain instead of calling one number.
All three QS-Edge configurations are rated for 5–35°C ambient — standard for server rooms and air-conditioned factory control cabinets. For deployments in unconditioned spaces (warehouses, outdoor enclosures, steel mills), QSCompute offers the Industrial Thermal Upgrade:
Deploying edge AI on the factory floor? Start with a pre-configured, burn-in tested server.
QSCompute's QS-Edge servers ship with GPU, CUDA, storage, and networking pre-installed — plug in, push your model, and start inferring. All three configurations in stock with 3–7 day lead time.
Contact: +86 189-9192-7716 | info@qscompute.com