Core offering
Bare Metal GPU as a Service
Dedicated NVIDIA GPU servers: single-tenant, full control, ready for production AI.
Shared GPU instances mean variable performance, capacity you can't count on, and costs that climb with every hour of usage. ApexInfer's Bare Metal GPU service gives you dedicated physical servers with the latest NVIDIA accelerators. There's no virtualisation overhead and no noisy neighbours, and you get full root access to the hardware. Whether you're pre-training a foundation model, fine-tuning on proprietary data or serving inference at scale, we configure the server around your workload. Then we either hand you the keys or run it for you.
GPU options
| GPU | Architecture | GPU memory | Best suited for |
|---|---|---|---|
| NVIDIA B300 | Blackwell Ultra (HGX 8-GPU, NVLink) | 288 GB HBM3e | Frontier-scale training, large-context and reasoning-model inference, very large MoE models |
| NVIDIA H200 | Hopper (HGX 8-GPU, NVLink) | 141 GB HBM3e | LLM training and fine-tuning, high-throughput inference for 70B+ models |
| NVIDIA RTX PRO 6000 | Blackwell Server Edition (PCIe) | 96 GB GDDR7 ECC | Mid-to-large model inference, multimodal and vision AI, rendering, graphics VDI |
| NVIDIA L40S | Ada Lovelace (PCIe) | 48 GB GDDR6 ECC | Cost-efficient inference, fine-tuning of small to mid-size models, video and graphics |
Configurations (GPUs per server, CPU, RAM, NVMe, networking) and availability on request.
What's included
- Single-tenant servers with NVIDIA B300, H200, RTX PRO 6000 and L40S GPUs
- Configuration sized to your model, batch size and throughput targets
- High-bandwidth networking for multi-node training (InfiniBand / RoCE options)
- High-performance NVMe local storage, with shared storage options
- OS, drivers, CUDA and container runtime installed and validated
- Optional AI stack: Kubernetes GPU scheduling, and vLLM, SGLang or TensorRT-LLM serving
- Hardware monitoring, health checks and replacement handling
- Optional fully managed operations through our Managed Services
Outcome
Predictable GPU performance, dedicated capacity and a cost you can plan around.