有比；单片的

https://www.tomshardware.com/tech-industry/artificial-intelligence/deepseek-research-suggests-huaweis-ascend-910c-delivers-60-percent-nvidia-h100-inference-performance?utm_source=chatgpt.com

 

 

Ascend 910C vs NVIDIA H100 vs AMD MI300X

Specification	Huawei Ascend 910C	NVIDIA H100 (SXM5)	AMD MI300X
FP16 Performance	800 TFLOPS	989 TFLOPS (Sparsity: 1979)	1307.4 TFLOPS (Sparsity: 2614.8)
INT8 Performance	~1600 TOPS	~1979 TOPS (Sparsity: 3958)	2614.9 TOPS (Sparsity: 5229.8)
Memory	128GB HBM3	80GB HBM3	192GB HBM3e
Memory Bandwidth	3.2 TB/s	3.35 TB/s	5.3 TB/s
Power Consumption (TDP)	~310W (potentially higher)	Up to 700W	750W
Software Ecosystem	CANN, MindSpore, PyTorch, TensorFlow	CUDA, cuDNN, TensorRT	ROCm, HIP

Note: NVIDIA and AMD often quote performance with sparsity features; dense compute figures are used for a more direct comparison where possible.

 

 

 

Feature	Huawei Ascend 920 (Claimed/Projected)	NVIDIA H100 (SXM/PCIe)
Architecture	Huawei Da Vinci Architecture (Chiplet-based)	NVIDIA Hopper
Process Node	SMIC 6nm (Projected)	TSMC 4nm (Custom)
FP16/BF16 Compute	900 TFLOPS (BF16, per card)	1,513 TFLOPS (BF16, without sparsity)
FP8 Compute	Not widely published/clear for 920, but its predecessor (910C) is lower.	3,026 TFLOPS (FP8, without sparsity)
Memory Bandwidth	4.0 TB/s (HBM3)	3.35 - 3.9 TB/s (HBM3)
GPU Memory (VRAM)	Likely high (The predecessor, 910C, has 128GB HBM3)	80GB (HBM3)
Software Ecosystem	CANN (Requires porting, less mature)	CUDA (Industry standard, highly mature)
Primary Market	China (Strong domestic focus)	Global

所有跟帖： 

• 主要是生态CUDA依赖太高，切换成本很大 -霸天虎- ♂ (186 bytes) () 11/03/2025 postreply 23:18:01

• 老共应该在高校提供免费的国内版本，让学生习惯中国的AI生态。年轻人习惯比较快 -硬码工- ♂ (0 bytes) () 11/04/2025 postreply 00:30:34

• 没有那么容易的 -霸天虎- ♂ (253 bytes) () 11/04/2025 postreply 01:17:38