Subj : Huawei Atlas 950 SuperPoD vs Nvidia DGX SuperPOD vs AMD Instinct To : All From : TechnologyDaily Date : Sun Oct 05 2025 15:15:09 Huawei Atlas 950 SuperPoD vs Nvidia DGX SuperPOD vs AMD Instinct Mega POD: How do they compare? Date: Sun, 05 Oct 2025 14:04:00 +0000 Description: Huawei, Nvidia, and AMD clash over AI supercomputing dominance with radically different designs, performance philosophies, and rollout timelines. FULL STORY ======================================================================Huawei stacks thousands of NPUs to show brute-force supercomputing dominance Nvidia delivers polish, balance, and proven AI performance that enterprises trust AMD teases radical networking fabrics to push scalability into new territory The race to build the most powerful AI supercomputing systems is intensifying, and major brands now want a flagship cluster that proves it can handle the next generation of trillion-parameter models and data-heavy research. Huaweis recently-announced Atlas 950 SuperPoD , Nvidias DGX SuperPOD, and AMDs upcoming Instinct MegaPod each represent different approaches to solving the same problem. They all aim to deliver massive compute, memory, and bandwidth in one scalable package, powering AI tools for generative models, drug discovery, autonomous systems, and data-driven science. But how do they compare? Huawei Ascend 950 vs Nvidia H200 vs AMD MI300 Instinct Category Huawei Ascend 950DT NVIDIA H200 AMD Radeon Instinct MI300 Chip Family / Name Ascend 950 series H200 (GH100, Hopper) Radeon Instinct MI300 (Aqua Vanjaram) Architecture Proprietary Huawei AI accelerator Hopper GPU architecture CDNA 3.0 Process / Foundry Not yet publicly confirmed 5 nm (TSMC) 5 nm (TSMC) Transistors Not specified 80 billion 153 billion Die Size Not specified 814 mm 1017 mm Optimization Decode-stage inference & model training General-purpose AI & HPC acceleration AI/HPC compute acceleration Supported Formats FP8, MXFP8, MXFP4, HiF8 FP16, FP32, FP64 (via Tensor/CUDA cores) FP16, FP32, FP64 Peak Performance 1 PFLOPS (FP8 / MXFP8 / HiF8), 2 PFLOPS (MXFP4) FP16: 241.3 TFLOPS, FP32: 60.3 TFLOPS, FP64: 30.2 TFLOPS FP16: 383 TFLOPS, FP32/FP64: 47.87 TFLOPS Vector Processing SIMD + SIMT hybrid, 128-byte memory access granularity SIMT with CUDA and Tensor cores SIMT + Matrix/Tensor cores Memory Type HiZQ 2.0 proprietary HBM (for decode & training variant) HBM3e HBM3 Memory Capacity 144 GB 141 GB 128 GB Memory Bandwidth 4 TB/s 4.89 TB/s 6.55 TB/s Memory Bus Width Not specified 6144-bit 8192-bit L2 Cache Not specified 50 MB Not specified Interconnect Bandwidth 2 TB/s Not specified Not specified Form Factors Cards, SuperPoD servers PCIe 5.0 x16 (server/HPC only) PCIe 5.0 x16 (compute card) Base / Boost Clock Not specified 1365 / 1785 MHz 1000 / 1700 MHz Cores / Shaders Not specified CUDA: 16,896, Tensor: 528 (4th Gen) 14,080 shaders, 220 CUs, 880 Tensor cores Power (TDP) Not specified 600 W 600 W Bus Interface Not specified PCIe 5.0 x16 PCIe 5.0 x16 Outputs None (server use) None (server/HPC only) None (compute card) Target Scenarios Large-scale training & decode inference (LLMs, generative AI) AI training, HPC, data centers AI/HPC compute acceleration Release / Availability Q4 2026 Nov 18, 2024 Jan 4, 2023 The philosophy behind each system What makes these systems fascinating is how they reflect the strategies of their makers. Huawei is leaning heavily on its Ascend 950 chips and a custom interconnect called UnifiedBus 2.0 - the emphasis is on building out compute density at an extraordinary scale, then networking it together seamlessly. Nvidia has spent years refining its DGX line and now offers the DGX SuperPOD as a turnkey solution, integrating GPUs, CPUs , networking, and storage into a balanced environment for enterprises and research labs. AMD is preparing to join the conversation with the Instinct MegaPod, which aims to scale around its future MI500 accelerators and a brand-new networking fabric called UALink. While Huawei talks about exaFLOP levels of performance today, Nvidia highlights a stable, battle-tested platform, and AMD pitches itself as the challenger offering superior scalability down the road. At the heart of these clusters are heavy-duty processors built to deliver immense computational power and handle data-intensive AI and HPC workloads. Huaweis Atlas 950 SuperPoD is designed around 8,192 Ascend 950 NPUs, with reported peaks of 8 exaFLOPS in FP8 and 16 exaFLOPS in FP16 - so it is clearly aimed at handling both training and inference at an enormous scale. Nvidias DGX SuperPOD, built on DGX A100 nodes, delivers a different flavor of performance - with 20 nodes containing a total of 160 A100 GPUs, it looks smaller in terms of chip count. However, each GPU is optimized for mixed precision AI tasks and paired with high-speed InfiniBand to keep latency low. AMDs MegaPod is still on the horizon, but early details suggest it will pack 256 Instinct MI500 GPUs alongside 64 Zen 7 Verano CPUs. While its raw compute numbers are not yet published, AMDs goal is to rival or exceed Nvidias efficiency and scale, especially as it uses next-generation PCIe Gen 6 and 3-nanometer networking ASICs. Feeding thousands of accelerators requires staggering amounts of memory and interconnect speed. Huawei claims the Atlas 950 SuperPoD carries more than a petabyte of memory, with a total system bandwidth of 16.3 petabytes per second. This kind of throughput is designed to keep data moving without bottlenecks across its racks of NPUs. Nvidias DGX SuperPOD does not attempt to match such headline numbers, instead relying on 52.5 terabytes of system memory and 49 terabytes of high-bandwidth GPU memory, coupled with InfiniBand links of up to 200Gbps per node. The focus here is on predictable performance for workloads that enterprises already run. AMD, meanwhile, is targeting the bleeding edge with its Vulcano switch ASICs offering 102.4Tbps capacity and 800Gbps per tray external throughput. Combined with UALink and Ultra Ethernet, this suggests a system that will surpass current networking limits once it launches in 2027. One of the biggest differences between the three contenders lies in how they are physically built. Huaweis design allows for expansion from a single SuperPoD to half a million Ascend chips in a SuperCluster. There are also claims that an Atlas 950 configuration could involve more than a hundred cabinets spread over a thousand square meters. Nvidias DGX SuperPOD takes a more compact approach, with its 20 nodes integrated in a cluster style that enterprises can deploy without needing a stadium-sized data hall. AMDs MegaPod splits the difference, with two racks of compute trays plus one dedicated networking rack, showing that its architecture is centered around a modular but powerful layout. In terms of availability, Nvidias DGX SuperPOD is already on the market, Huaweis Atlas 950 SuperPoD is expected in late 2026, and AMDs MegaPod is planned for 2027. That said, these chips are fighting very different battles under the same banner of AI supercomputing supremacy. Huaweis Atlas 950 SuperPoD is a show of brute force, stacking thousands of NPUs and jaw-dropping bandwidth to dominate at scale, but its size and proprietary design may make it harder for outsiders to adopt. Nvidias DGX SuperPOD looks smaller on paper, yet it wins on polish and reliability, offering a proven platform that enterprises and research labs can plug in today without waiting for promises. AMDs MegaPod, still in development, has the makings of a disruptor, with its MI500 accelerators and radical new networking fabric that could tilt the balance once it arrives, but until then, it is a challenger talking big. Via Huawei , Nvidia , TechPowerUp You might also like Here are the best mobile workstations around today We've also listed the best monitors for every budget and resolution AI > Crypto - Bitcoin mining spinoff gets $700 million investment from Nvidia ====================================================================== Link to news story: https://www.techradar.com/pro/huawei-atlas-950-superpod-vs-nvidia-dgx-superpod -vs-amd-instinct-mega-pod-how-do-they-compare --- Mystic BBS v1.12 A49 (Linux/64) * Origin: tqwNet Technology News (1337:1/100) .