Nvidia H100 vs Huawei Ascend 910B: In‑Depth GPU Performance and Bandwidth Comparison

Overview

The following analysis, sourced from official vendor specifications, presents a side‑by‑side comparison of Nvidia and Huawei (HiSilicon/Ascend) GPUs that are commonly used for AI and high‑performance computing workloads.

Nvidia GPU Model Comparison (L2/T4/A10/A10G/V100)

The first chart lists the key specifications of Nvidia’s mainstream accelerator cards, including compute capability, memory size, and typical use cases. All numbers are taken from Nvidia’s product pages.

High‑End GPU Comparison: Nvidia A100/A800/H100/H800 vs Huawei Ascend 910B

The second chart expands the comparison to the latest data‑center class GPUs. A concise performance summary notes that the Nvidia H100 delivers roughly three times the performance of the A100 while costing about twice as much.

One‑sentence takeaway: H100 vs. A100 – 3× performance, 2× price.

Inter‑GPU Bandwidth: NVLink vs. HCCS

For multi‑GPU configurations, bandwidth between GPUs is a critical factor. An 8‑card Nvidia A800 system using NVLink provides a total bandwidth of 392 GB/s, comparable to the 400 GB/s offered by an 8‑card A100 mesh network. The Nvidia mesh topology can reach up to 600 GB/s in an 8 × A100 configuration.

In contrast, Huawei’s HCCS (Huawei Compute Communication Subsystem) employs a peer‑to‑peer topology without an NVSwitch‑like chip, resulting in a maximum bidirectional bandwidth of 56 GB/s per GPU pair.

Key Takeaways

Nvidia’s latest H100 offers the highest raw performance among the surveyed GPUs, but at a premium price.

Huawei’s Ascend 910B provides competitive performance with a different interconnect strategy (HCCS), which may be advantageous in power‑constrained or cost‑sensitive deployments.

When scaling to multiple GPUs, Nvidia’s NVLink mesh delivers substantially higher aggregate bandwidth than Huawei’s peer‑to‑peer HCCS.

Choosing the optimal GPU depends on the specific AI workload, budget, and required inter‑GPU communication bandwidth.

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