Huawei Ascend 310
Overview
The Huawei Ascend 310, codenamed Ascend-Mini, was the first Ascend processor to ship and the chip behind the entire Atlas 200 and Atlas 500 edge line. Announced by Eric Xu at Huawei Connect 2018, it pairs two Da Vinci AI cores with eight Arm Cortex-A55 host cores on a 12nm die of 104.4 square millimetres, and delivers 8 TFLOPS of FP16 or 16 TOPS of INT8 inside an 8 watt envelope, which is the efficiency point the whole design was built around. Alongside the AI cores it carries a substantial fixed-function media block, 16 channels of H.264 and H.265 decode plus one channel of encode, and an 8 MB on-chip buffer feeding the matrix units. Memory is 128-bit LPDDR4X, but capacity and bandwidth are deliberately left blank here because they are a board-level choice rather than a chip specification: Huawei's own Atlas 200 module ships the same silicon with either 4 GB or 8 GB. The Da Vinci Cube in this generation implements 4,096 FP16 MACs and 8,192 INT8 MACs and has no FP32 or BF16 matrix path at all, which is why this part carries only FP16 and INT8 figures.
Performance Metrics
Peak theoretical throughput by precision type
| Precision | Bits | Peak TFLOPS | Efficiency |
|---|---|---|---|
| INT8 | 8 | 16.0 | 2.000 TFLOPS/W |
| FP16 | 16 | 8.0 | 1.000 TFLOPS/W |
Power Specifications
TDP
8 W
Max Power
8 W
Power Connector
PCIe Slot
Cooling
Air
Memory Specifications
Capacity
--
Type
LPDDR4X
Bandwidth
--
Interface
128-bit
Hardware & Design
Form Factor
SoC
Architecture
Da Vinci
Process Node
12nm
Launch Year
2018
Variant
Standard
Market Segment
Professional
Die Size
104 mm²
Full Specifications
| Compute Engine | |
|---|---|
| Da Vinci AI Core | 2 |
| Chip Design | |
| Die Size | 104 mm² |
| Process Node | 12nm |
| Memory | |
| Memory Type | LPDDR4X |
| Interface Width | 128-bit |
| Interconnect & I/O | |
| PCIe | 3.0 x4 |
| Cache | |
| L2 Cache | 3 MB |
| Power & Thermal | |
| TDP | 8 W |
| Max Board Power | 8 W |
| Enterprise Features | |
| Compute APIs | CANN, MindSpore, MindX, ACL |
| General | |
| Form Factor | SoC |
| Architecture | Da Vinci |
| Launch Year | 2018 |
Documentation & Resources
Common Use Cases
The Huawei Ascend 310 is optimized for high-performance computing tasks with Da Vinci architecture delivering high 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 ProvidersSimilar GPUs
Stay Updated on GPU Releases
Get notified when new GPUs are added or specifications are updated.
No spam, unsubscribe anytime.