Server GPU Accelerators — NVIDIA & AMD for AI/ML

Data center GPUs for deep learning, HPC, and inference workloads

About GPU Accelerators

The explosion of AI, machine learning, and large language model training has made server-grade GPU accelerators the most in-demand data center component. We stock NVIDIA and AMD data center GPUs in both PCIe and SXM form factors — from the proven Tesla V100 for cost-effective inference to the NVIDIA H100 SXM5 for frontier model training.

Unlike consumer GPUs (GeForce/Radeon), server accelerators feature ECC HBM memory, passive cooling for rack-mount airflow, higher TDP envelopes (250W–700W), and enterprise driver support with long-term stability branches. They are designed for 24/7 operation in 1U–4U server chassis with proper GPU-to-GPU interconnect via NVLink or Infinity Fabric.

GPU Accelerator Lineup

NVIDIA Tesla V100 (32 GB HBM2) — proven workhorse for inference, still cost-effective
NVIDIA A100 (40 GB / 80 GB HBM2e) — Ampere architecture, MIG support, PCIe & SXM4
NVIDIA L40S (48 GB GDDR6) — Ada Lovelace for inference + graphics virtualization
NVIDIA H100 (80 GB HBM3) — Hopper architecture, the gold standard for LLM training
AMD Instinct MI210 (64 GB HBM2e) — ROCm-compatible alternative for open-source ML stacks
AMD Instinct MI250X (128 GB HBM2e) — multi-die design for HPC and scientific computing

GPU server builds require careful attention to power delivery, PCIe lane allocation, and cooling. Talk to our GPU specialists for chassis compatibility checks before ordering.

Featured GPU Accelerators Products

Browse all 109 GPU Accelerators SKUs.

Frequently Asked Questions

What is the difference between NVIDIA A100 PCIe and SXM?

The A100 PCIe is a standard PCIe Gen4 x16 card (250W TDP) that fits any compatible server. The A100 SXM4 (400W TDP) uses NVIDIA's proprietary SXM socket with NVLink 3.0 for 600 GB/s GPU-to-GPU bandwidth. SXM versions offer higher performance but require specific baseboard designs like the HGX A100. For single-GPU inference, PCIe is sufficient. For multi-GPU training, SXM with NVLink is strongly preferred.

Can I use a consumer GPU (GeForce) in a server?

Technically some GeForce cards physically fit in server PCIe slots, but they are not designed for data center use. Consumer GPUs lack ECC memory, have active fan cooling (incompatible with rack airflow), limited vGPU support, and NVIDIA's EULA restricts data center deployment. For production workloads, always use Tesla/A-series/L-series/H-series data center GPUs.

How much power does a GPU server need?

A single A100 PCIe draws 250W, while an H100 SXM5 draws up to 700W. A 4-GPU A100 server typically needs 2× 2000W PSUs, and an 8-GPU H100 DGX/HGX system requires 10+ kW. Always verify your rack PDU capacity, breaker ratings, and cooling infrastructure before deploying GPU servers.