What Is an AI Compute Card? From Terminology to Selection (2026 Guide)
What Is an AI Compute Card
A compute card (also called an accelerator card or AI card) is a board-level product that delivers AI training and inference compute: an AI accelerator chip (GPU / NPU / TPU / ASIC) packaged with memory, power delivery and cooling into a standardized card that plugs into servers or workstations. Common form factors include PCIe cards, SXM/OAM modules, and rack-scale supernodes.
Compared with consumer "graphics cards", compute cards have no display outputs, emphasize memory capacity and compute density, and target data center features such as ECC memory, virtualization, and multi-card interconnect.
Compute Cards vs GPU / NPU / TPU
These terms are easy to confuse: GPU / NPU / TPU / ASIC describe chip architecture types, while compute card describes the product form factor. The same board can be called both a GPU card and an AI accelerator card.
| Term | Common meaning | Deep dive |
|---|---|---|
| GPU | General parallel acceleration, richest ecosystem | GPU architecture |
| TPU | Tensor processing (originated by Google) | TPU architecture |
| NPU | Neural-network specific, common at the edge | NPU architecture |
| ASIC | Custom silicon (Trainium, Dojo, etc.) | ASIC architecture |
| Compute card | The board-level umbrella term | This page + comparison table |
Six Selection Dimensions
- Compute and precision: every card has FP16 / FP8 / FP4 numbers, split into sparse vs dense (sparse is roughly 2x dense). Always compare at the same precision and sparsity convention — every card on this site states its convention.
- Memory capacity and bandwidth: determines model size. Training cares about capacity (HBM3e/HBM4); inference throughput is bandwidth-driven.
- Interconnect: NVLink / NVLink 6, Huawei MTLink, Cambricon MLU-Link, UALink, etc. determine multi-card scaling; without a fast interconnect you are limited to single-card inference.
- Power and cooling: TDP ranges from tens of watts (edge cards) to 1,400W (B300 Ultra), driving rack power and cooling design.
- Software ecosystem: CUDA remains the de facto standard; domestic cards map to CANN (Ascend), DTK (Hygon), MUSA (Moore Threads) — factor migration cost into the decision.
- Price and TCO: sticker price aside, electricity, utilization and depreciation define real cost — run your numbers in the TCO calculator.
What to Pick by Scenario
| Scenario | Representative cards | What matters |
|---|---|---|
| LLM training | H100 / H200 / B200 / Ascend 910C / MLU690 | FP16/FP8 compute + fast interconnect + cluster solutions |
| LLM inference | H20 / L40S / Kunlunchip P800 / MTT S5000 | Memory bandwidth and cost per token |
| Edge and low power | L4 / L2 / T4 / RK3588 | Power, form factor, price-performance |
More category entry points: Data center AI training · Data center AI inference · Edge AI
Quick Data Access
- Full comparison table: 81 featured + all 252 cards — FP8/INT8 compute, memory, TDP in one table
- TCO calculator: model multi-year total cost of ownership and cost per token
- Launch timeline: release cadence across generations
FAQ
Compute card vs graphics card? Compute cards target data centers: no display outputs, ECC memory and multi-card interconnect as standard; consumer GPUs target graphics and gaming. Precision conventions also differ — data center cards are read at FP16/FP8/FP4.
Which compute number should I compare? Align precision first (FP16/FP8/FP4), then sparse vs dense, and always read it together with memory bandwidth — inference throughput is usually bandwidth-bound rather than compute-bound.
Representative domestic (Chinese) compute cards? Training: Ascend 910C, Cambricon MLU690, Moore Threads MTT S5000. Inference: Kunlunchip P800, Hygon DCU, Enflame Yunsui series. See the comparison table.