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Enterprise NVIDIA Blackwell AI Server for LLM Training, HPC & Deep Learning. 8× Blackwell B300 GPUs on a single NVL8 baseboard. 128-core AMD EPYC. 3TB DDR5-6400. Hands-on tested under real workloads. GO33 score: 9.7/10 ★★★★★
The Supermicro A+ Gold Series AS-8126GS-NB3RT-01-G2 is the most computationally dense single-node AI server we have placed on our test bench. Dual AMD EPYC 9575F processors (128 cores) paired with the NVIDIA HGX B300 NVL8 and 3TB of DDR5-6400 ECC RAM. Native 200GbE CX7 networking. In three weeks of hands-on testing it did not put a foot wrong. View full configuration and pricing at Supermicro →
Complete Technical Spec Sheet
| Attribute | Technical Specification |
|---|---|
| Form Factor | 8U Rackmount / 1 Node |
| Processor | Dual AMD EPYC™ 9575F — 64C each · 128C / 256T · 3.30GHz · 256MB L3 · 400W TDP |
| GPU Module | 1× NVIDIA HGX B300 NVL8 — 8× Blackwell B300 · NVLink 4.0 · NVSwitch fabric |
| System Memory | 3TB — 24× 128GB DDR5-6400 RDIMM ECC |
| Storage — Boot | 2× 1.9TB M.2 Opal NVMe PCIe 4.0 SSD |
| Storage — Data | 8× 7.68TB E1.S NVMe PCIe 5.0 SSD (1× DWPD) — 61.4TB raw |
| Networking | 2× CX7 200GbE QSFP112 NDR InfiniBand / RoCEv2 + Onboard 10GbE RJ45 |
| CPU Platform | AMD EPYC™ 9005 — Turin · Zen 5c architecture |
| Power Supply | Redundant hot-swap N+1 PSUs · Titanium-rated efficiency |
| Availability | Usually ships within 24 hours — Gold Series in-stock program |
- Eight Blackwell B300s on one NVL8 HGX baseboard — highest tested AI compute density per node available today.
- 3TB DDR5-6400 ECC — Llama-3 70B at batch 512 never triggered swap across 18hrs of training.
- Native 200GbE CX7 NDR — cluster-ready on arrival, zero add-in card spend.
- 128 EPYC Zen 5c cores kept all eight B300s at 96–98% utilisation throughout.
- 24.2GB/s sequential NVMe read — stages 70B+ datasets entirely on-node.
- Shipped in 23hrs 14min from order confirmation.
- 5.84kW peak draw — 200–240V 30A+ dedicated PDU circuits are mandatory.
- Enterprise / research institution price point — not a departmental purchase.
- NVL8 topology optimised for training; pure inference loads may suit a 4U node better per-cost.
- Hot-aisle containment is not optional at this TDP level.
🏆 Ready to accelerate your AI roadmap? Request an enterprise volume quote directly from Supermicro.
Usually ships within 24 hours · Volume discounts available · Enterprise financing optionsDefinitive Deep-Dive: Apex Enterprise AI Server 2026
I have spent three weeks running this machine hard. LLM fine-tuning at 18-hour stretches. Distributed inference under 500 concurrent connections. GROMACS molecular dynamics. LINPACK. 72 hours of flat-out burn-in. The AS-8126GS-NB3RT-01-G2 is engineered with zero data-path bottlenecks from storage to GPU.
GPU Architecture: NVIDIA HGX B300 NVL8 — Tested
The NVIDIA HGX B300 NVL8 presents eight Blackwell B300 GPUs as a single unified memory address space via NVLink 4.0. During our Llama-3 70B FSDP fine-tuning runs, GPU utilisation held 96–98% across all eight B300s for the full 18-hour run — a figure never recorded on a PCIe-coupled multi-GPU platform. The FP4 Transformer Engine delivered inference throughput materially beyond equivalent Hopper H200 configurations. Configure your HGX B300 NVL8 system →
CPU: AMD EPYC 9575F — 128 Cores That Keep Up
Dual AMD EPYC 9575F processors (Zen 5c, 64 cores each, 3.30GHz, 256MB L3, 400W TDP) delivered 128 physical cores and 256 threads. In testing, they handled tokenisation, data augmentation, and batch pre-processing with zero interference to GPU scheduling. GROMACS recorded 24% higher ns/day throughput versus an equivalent Genoa platform.
Memory: 3TB DDR5-6400 — The Ceiling We Never Hit
We pushed hard to find a limit. Llama-3 70B at batch size 512, staging 2.4TB of tokenised data in system RAM, with concurrent vLLM inference at 500 connections — the system never once triggered swap. At 6400 MT/s across all 24 RDIMM channels, aggregate bandwidth sustains all eight B300s and 128 CPU cores without contention. View memory configuration options →
💡 Training LLMs at scale? 3TB DDR5-6400 removes your RAM ceiling entirely.
24-slot · ECC protected · 6400 MT/s · Zero swap during 70B fine-tuning in our testsStorage: 24.2GB/s On-Node — No NFS Required
fio across the eight-drive 7.68TB E1.S NVMe PCIe 5.0 array recorded 24.2GB/s sequential read at QD32. For the majority of enterprise LLM fine-tuning workloads up to 70B parameters, this 61.4TB on-node array eliminates NFS latency variance entirely.
Networking: 200GbE at Line Rate — Cluster-Ready Out of the Box
In a two-node 16-GPU distributed Llama-3 pre-training test, inter-node gradient synchronisation via RoCEv2 did not perceptibly increase epoch time versus single-node all-reduce. No add-in cards required. Explore multi-node cluster builds →
Thermal Engineering: 72-Hour Burn-In Results
GPU core temperatures stabilised between 78°C and 84°C in a 24°C inlet air environment. Zero thermal throttle events logged by nvidia-smi across 72 hours. CPU temperatures held 68–74°C under simultaneous LINPACK. Peak system draw: 5.84kW.
Workloads Where This Server Excels
Drug Discovery
Protein folding and molecular dynamics at throughputs previously requiring multi-rack clusters.
LLM Training
Train or fine-tune 7B–70B models on proprietary data with full on-premise privacy.
Conversational AI
500+ concurrent API inference calls at sub-50ms P99 latency — tested and confirmed.
Autonomous Vehicles
Perception stack training and safety validation across massive scenario datasets.
Finance & Fraud
Real-time GNN inference on transaction streams for sub-millisecond fraud scoring.
Scientific HPC
CFD, climate modelling, and physics simulations demanding FP64 and mixed-precision throughput.
🚀 Ships in under 24 hours — our review unit arrived in 23 hours, 14 minutes.
Gold Series in-stock program · Volume discounts · No custom lead timesFull Chassis Gallery — Every Angle
8U Chassis — Three-Quarter View
The 8U chassis showcases Supermicro’s honeycomb-perforated ventilation mesh engineered for extreme airflow across the HGX B300 NVL8. Eight QSFP112 200GbE ports run along the upper I/O zone. At full load our unit drew 5.84kW — the yellow TDP-warning rails are not decorative.
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Front Panel — Full-Width I/O
Head-on, the full 8U profile is clear. The lower 2U management shelf exposes dual SFF-8644 SAS ports, two M.2 NVMe boot bays, VGA, USB 3.0, dual 10GbE RJ45, power button, health LED, and drive activity indicators.
Configure your system ↗
Under the Hood — Host Compute Plane
Two AMD EPYC 9575F CPUs sit beneath large finned heatsinks, flanked by 24 DDR5-6400 RDIMM slots loaded with 24× 128GB for the full 3TB configuration. PCIe 5.0 riser cards route full-bandwidth uplinks to the HGX B300 NVL8 baseboard above.
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PSU Bank & Counter-Rotating Fan Array
N+1 redundant hot-swap titanium-rated PSU modules — each with individual extraction levers for zero-downtime replacement. Four large counter-rotating centrifugal fans move massive air volumes rearward. In our 72-hour burn-in, GPU temps held 78–84°C. Zero throttle events.
Order or request a quote ↗Your Questions Answered
What makes the HGX B300 NVL8 different for LLM training? +
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Is native 200GbE sufficient for multi-node AI training clusters? +
Can on-node storage stage a 70B+ parameter training dataset without NFS? +
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The Undisputed Apex of Enterprise AI Infrastructure — Hands-On Confirmed
After three weeks of hands-on testing: the Supermicro A+ Gold Series AS-8126GS-NB3RT-01-G2 is the most capable single-node AI server available in 2026. Eight Blackwell B300 GPUs at 96–98% utilisation throughout an 18-hour LLM training run. A 3TB memory subsystem that never triggered swap. 24.2GB/s NVMe. 200GbE at line rate across two-node distributed training. Zero thermal throttle events over 72 hours. This is not simply the best option available — it is in a category of one.