Skip to main content

Huawei Ascend 910C vs Moore Threads 摩尔线程 MTT S5000 (国产 GPU 训练): Spec Comparison & Buyer's Guide

In AI infrastructure selection, Huawei Ascend 910C and Moore Threads 摩尔线程 MTT S5000 (国产 GPU 训练) 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 910CMoore Threads 摩尔线程 MTT S5000 (国产 GPU 训练)
VendorHuaweiOther
ArchitectureDa VinciMUSA
Process7nm -classTSMC 6nm
Release Date2024-04-242025-02-12
FP8 Compute——
FP16 Compute——
FP32 Compute—62.5 TFLOPS
INT8 Compute—2,000 TOPS
Memory Type——
Memory Capacity8× HBM380GB GDDR6X
Memory Bandwidth—1.6 TB/s
TDP Power310 W300 W

Key Differences​

  • Power: Moore Threads 摩尔线程 MTT S5000 (国产 GPU 训练) has a TDP of 300 W, lower than Huawei Ascend 910C's 310 W, friendlier to datacenter PUE and cooling.
  • Memory capacity: Moore Threads 摩尔线程 MTT S5000 (国产 GPU 训练) packs 80GB GDDR6X, more than Huawei Ascend 910C's 8× HBM3, more comfortable for single-card hosting of very large models.

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, Moore Threads 摩尔线程 MTT S5000 (国产 GPU 训练) 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 Moore Threads 摩尔线程 MTT S5000 (国产 GPU 训练)?​

The core difference is architecture and compute density: Huawei Ascend 910C uses Da Vinci, FP8 ~No public FP8 data, memory 8× HBM3; Moore Threads 摩尔线程 MTT S5000 (国产 GPU 训练) uses MUSA, FP8 ~No public FP8 data, memory 80GB GDDR6X. 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 Moore Threads 摩尔线程 MTT S5000 (国产 GPU 训练) differ in memory capacity?​

Huawei Ascend 910C is 8× HBM3, Moore Threads 摩尔线程 MTT S5000 (国产 GPU 训练) is 80GB GDDR6X; the gap directly affects loadable model size and context length.