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Huawei Ascend 910C vs MetaX 沐曦 曦云 C600 (2025): Spec Comparison & Buyer's Guide

In AI infrastructure selection, Huawei Ascend 910C and MetaX 沐曦 曦云 C600 (2025) are two accelerators frequently compared. This article contrasts them item by item — architecture, compute, memory, power, and release cadence — to help you quickly judge which fits training or inference workloads.

Spec Comparison Table

VendorHuawei Ascend 910CMetaX 沐曦 曦云 C600 (2025)
VendorHuaweiOther
ArchitectureDa VinciXCORE 1.5
Process7nm -class7nm
Release Date2024-04-242025 10
FP8 Compute1000 TFLOPS
FP16 Compute
FP32 Compute15 TFLOPS
INT8 Compute
Memory Type
Memory Capacity8× HBM3
Memory Bandwidth3.6 TB/s
TDP Power310 W400 W

Key Differences

  • Power: Huawei Ascend 910C has a TDP of 310 W, lower than MetaX 沐曦 曦云 C600 (2025)'s 400 W, friendlier to datacenter PUE and cooling.

Selection Advice

  • When chasing extreme single-card compute and a mature toolchain, prioritize Huawei Ascend 910C; if budget, power wall, or local support are hard constraints, MetaX 沐曦 曦云 C600 (2025) often fits better. Use this site's AI Compute Card Comparison Tool to validate multiple chips side-by-side before deciding.

FAQ

What are the main differences between Huawei Ascend 910C and MetaX 沐曦 曦云 C600 (2025)?

The core difference is architecture and compute density: Huawei Ascend 910C uses Da Vinci, FP8 ~No public FP8 data, memory 8× HBM3; MetaX 沐曦 曦云 C600 (2025) uses XCORE 1.5, FP8 ~1000 TFLOPS, memory —. See the comparison table above.

What is the TDP (power) of Huawei Ascend 910C?

Huawei Ascend 910C has a TDP of 310 W; actual whole-system power also includes board, fans, and PUE.

Which is better for large-model training / inference?

Training values memory capacity, bandwidth, and multi-card interconnect; inference values single-card throughput and power efficiency. Combine the "Key Differences" and "Selection Advice" above with your batch size, model size, and SLA.

How much do Huawei Ascend 910C and MetaX 沐曦 曦云 C600 (2025) differ in memory capacity?

Huawei Ascend 910C is 8× HBM3, MetaX 沐曦 曦云 C600 (2025) is —; the gap directly affects loadable model size and context length.