NVIDIA GPUs and Accelerators

Key takeaways

  • 223 listings match this collection right now.
  • NVLink memory pooling is limited to specific cards across the line, the V100 and the larger Quadro RTX cards, not the T4 or other workstation cards.
  • Tesla and A-series cards run NVIDIA's datacenter driver branch, tied to server-grade GPU management tools like DCGM, while Quadro and RTX A cards run a separate workstation branch instead.
  • Only the highest-end datacenter cards in this family, like the V100, carry HBM memory for its bandwidth, while the T4 and every Quadro RTX card use cheaper, lower-bandwidth GDDR6 instead.

NVIDIA cards in the catalogue split into datacenter accelerators (Tesla and A-series, passively cooled, no display outputs) and workstation cards (Quadro and RTX A, actively cooled, with DisplayPort outputs). Inference and video transcoding run well on 16GB Turing and Ampere cards; training and large models want 32GB to 80GB of HBM.

223 listings in this collection, filtered from Graphics Cards. The part number and manufacturer are stated on every listing, and the condition wherever the catalogue records one. For quantity, request a volume quote.

How to choose gpus and accelerators

Datacenter cards (Tesla, A-series) are passively cooled and expect server airflow; workstation cards (Quadro, RTX A) carry their own fans and display outputs. Match the slot (PCIe generation and width), the auxiliary power the card needs, and the memory the workload needs: inference and video work fits in 16 GB to 24 GB, training and large models want 32 GB to 80 GB.

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