Huawei logo

Huawei Atlas 950 SuperPoD

pod
1024× Ascend 950DT
UnifiedBus 2.0 (Lingqu) all-optical, 1.72 PB/s aggregate scale-up, 64 x 1.68 TB/s bidirectional per cabinet
2026
FP16
—
EFLOPS
FP8
1.00
EFLOPS
Power
—
kW Total
Memory
98304
GB Total

Huawei's 1,024-card Atlas 950 SuperPoD as shown at WAIC 2026: 16 compute cabinets plus 4 UnifiedBus interconnect cabinets (44OU each), up to 1,024 Ascend 950DT, 1,024 x 96 GB on-chip memory at 4.0 TB/s, 1 EFLOPS mxFP8/FP8/HiF8 and 2 EFLOPS mxFP4, 1.72 PB/s total scale-up bandwidth, 256 TB globally addressable memory, fully liquid cooled. Huawei's HC2025 keynote of 18 September 2025 described an 8,192-card configuration of the same product delivering 8 EFLOPS FP8 and 16 EFLOPS FP4. Huawei states no dense or sparse basis for any of these figures. POWER: Huawei publishes no power rating for this machine at either the 1,024-card or the 8,192-card scale. Its only first-party page for the 1,024-card unit (huawei.com/cn/news/2026/7/atlas-950-superpod) carries no watt line at all, the e.huawei.com SuperPoD catalogue does not list the Atlas 950 yet, and Huawei has never published a chip-level power figure for any Ascend part. The 100 kW previously stored here cannot be a whole-SuperPoD rating, since 100 kW over 1,024 cards is 98 W per card; a per-cabinet reading would be arithmetically plausible against the 16 compute cabinets, but no Huawei source has been found for it at any scope, so nothing is stored rather than a rescaled guess.

We will point you at suppliers who have it. Free, and no signup.

FP16
—
EFLOPS
FP8
1.00
EFLOPS
FP4
2.00
EFLOPS

System Details

GPU Configuration

GPU Count: 1024 GPUs
Architecture: Da Vinci v3
Interconnect: UnifiedBus 2.0 (Lingqu) all-optical, 1.72 PB/s aggregate scale-up, 64 x 1.68 TB/s bidirectional per cabinet

System Specifications

Form Factor: pod
Total Power: —
Total Memory: 98.3 TB
Memory Bandwidth: 4.1 PB/s

Precision Performance Breakdown

PrecisionSystem PerformancePer GPUEfficiency
0.00 EFLOPS 0 TFLOPS —
1.00 EFLOPS 977 TFLOPS —
2.00 EFLOPS 1,953 TFLOPS —

Where a vendor states a basis, the figure shown is the dense one, and any figure it publishes is listed separately. Vendors commonly headline the sparse number instead: for NVIDIA's 2:4 structured sparsity that is exactly twice the dense figure, though other vendors' sparse modes do not all follow that ratio.

Powered by Huawei Ascend 950DT

This system utilizes 1024 × Huawei Ascend 950DT GPUs, each delivering exceptional performance for AI and HPC workloads.

Per GPU TDP

—

Per GPU Memory

144 GB

Process Node

—

Architecture

Da Vinci v3

Documentation & Resources

Flopper Spec Sheet

Huawei Atlas 950 SuperPoD specifications, generated from our database. Printable.

Vendor product page

Huawei Atlas 950 SuperPoD documentation

View ↗

Huawei HiSilicon Da Vinci architecture, Hot Chips 31 (2019)

Huawei • 2019-08-19

View Document ↗

Typical Use Cases

AI/ML Training
High-Performance Computing
Data Analytics

The Huawei Atlas 950 SuperPoD runs 1024× Ascend 950DT GPUs over UnifiedBus 2.0 (Lingqu) all-optical, 1.72 PB/s aggregate scale-up, 64 x 1.68 TB/s bidirectional per cabinet, delivering 1.00 EFLOPS FP8 dense.

© 2026 Flopper.io - Compare the hardware powering AI