HU
Huawei Ascend 910C
Da Vinci v2 (dual-die) OAM 2025 SMIC 7nm
FP32
50.0
TFLOPS
VRAM
128 GB
HBM2e
TDP
600 W
83.3 TFLOPS/kW
Bandwidth
3.2 TB/s
memory
Performance Metrics
Peak theoretical throughput by precision type
| Precision | Description | Bits | Peak TFLOPS | Efficiency |
|---|---|---|---|---|
| INT8 | 8-bit integer | 8 | 1600.0 | 2.667 TFLOPS/W |
| FP16 | 16-bit floating point | 16 | 800.0 | 1.333 TFLOPS/W |
| BF16 | Brain Float 16 | 16 | 780.0 | 1.300 TFLOPS/W |
| FP32 | 32-bit floating point | 32 | 50.0 | 0.083 TFLOPS/W |
FP16 Efficiency
1.333 TFLOPS/W
800.0 TFLOPS / 600W
FP32 Efficiency
0.083 TFLOPS/W
50.0 TFLOPS / 600W
Power Specifications
TDP
600 W
Max Power
690 W
Power Connector
PCIe 16-pin
Cooling
Air
Memory Specifications
Capacity
128 GB
Type
HBM2e
Bandwidth
3200 GB/s
Interface
--
Hardware & Design
Form Factor
OAM
Architecture
Da Vinci v2 (dual-die)
Process Node
SMIC 7nm
Launch Year
2025
Variant
Standard
Market Segment
Data Center
Full Specifications
| Memory | |
|---|---|
| VRAM | 128 GB |
| Memory Type | HBM2e |
| Bandwidth | 3.2 TB/s |
| Interconnect & I/O | |
| GPU-to-GPU | Unified Bus (UB) |
| Interconnect Bandwidth | 350 GB/s |
| Power & Thermal | |
| TDP | 600 W |
| General | |
| Form Factor | OAM |
| Architecture | Da Vinci v2 (dual-die) |
| Launch Year | 2025 |
Documentation & Resources
Common Use Cases
General Compute AI/ML Workloads Data Processing
The Huawei Ascend 910C is optimized for high-performance computing tasks with Da Vinci v2 (dual-die) architecture delivering 50 TFLOPS of compute power.
Where to Rent
Compare cloud providers offering on-demand GPU instances for AI training, inference, and HPC workloads.
Browse GPU Cloud ProvidersStay Updated on GPU Releases
Get notified when new GPUs are added or specifications are updated.
Loading verification...
No spam, unsubscribe anytime.