Huawei vs NVIDIA: AI Chip & Super Node Roadmap Showdown (2020–2028)

From Single-Chip to System-Level Super Nodes

As large-model parameter counts grow exponentially, AI infrastructure competition has shifted from single-chip performance to system-level super-node (SuperPod) interconnect. Based on public roadmaps and BOM topologies as of October 2026, this article dissects the divergent evolution paths of NVIDIA and Huawei — the two dominant players in global AI compute — across chip architecture, interconnect technology, and system integration.

1. Chip Roadmaps: From Point Breakthroughs to System-Level Leaps

1.1 NVIDIA: Process & Interconnect Brute Force

NVIDIA follows a clear two-year cadence , synchronizing upgrades in low-precision compute (FP8/FP4) , HBM bandwidth , and NVLink interconnect .

Architecture progression: Ampere (A100) → Hopper (H100, H200) → Blackwell (B200, B300) → planned Rubin (VR200).

Key parameter trends:

Compute: FP16 dense performance jumps from 312 TFLOPS (A100) to 4 PFLOPS (Rubin).

Memory: HBM capacity grows from 80 GB (HBM2e) to 288 GB (HBM4); bandwidth from 2.0 TB/s to 19.2 TB/s.

Interconnect: NVLink bandwidth rises from 600 GB/s (A100) to 3 TB/s (Rubin).

2026 flagship: Vera Rubin NVL72 — 72 GPUs per rack, single-GPU FP4 sparse inference at 3,600 PFLOPS, rack-level token throughput of 1,183,327 tokens/s.

1.2 Huawei Ascend: From Catch-Up to Self-Reliant Ecosystem

Huawei emphasizes system-level super-node interconnect and self-controllability , using cluster scale to offset single-chip process gaps.

Architecture progression: Ascend 910B (baseline) → 910C → planned 950DT/960/970.

Key milestones:

2025 (Ascend 910C): Volume production; ~800 TFLOPS FP16, 128 GB HBM.

2026 Q4 (Ascend 950DT): HiZQ 2.0 memory, 8 EFLOPS FP8, 16 PB/s interconnect, supports Lingqu (UBoE) ecosystem.

2027–2028 (960/970): Expected double compute, memory capacity, and interconnect ports.

2. System-Level Showdown: SuperPod Architecture Analysis

Single-chip performance no longer measures real-world AI cluster capability; Scale-Up (vertical scaling) interconnect becomes the decisive factor for large-model training efficiency.

2.1 Huawei: Winning with Super-Node Cluster Scale

Huawei demonstrates massive system integration via CloudMatrix384 and Atlas 950 SuperPod .

CloudMatrix384 (16-cabinet super node): 384× Ascend 910C linked by UnifiedBus (unified memory addressing). Delivers 300 PFLOPS compute, 48 TB/s interconnect bandwidth.

Atlas 950 SuperPod (160-cabinet system, 2026 Q4): 8,192× Ascend 950DT. System interconnect bandwidth reaches 16 PB/s ; all-optical UBoE/RoCE drastically cuts communication latency.

Advantage: Ultra-large cluster scale yields leadership in aggregate compute and total memory, suited for ultra-large model training.

2.2 NVIDIA: Moat Built on Single-Rack Density & NVLink

NVIDIA focuses on extreme intra-rack density and interconnect bandwidth.

GB300 NVL72: 72× B300 GPUs + 36× Grace CPUs per rack. 5th-gen NVLink (1.8 TB/s) enables full mesh. Rack FP4 sparse compute: 1,440 PFLOPS; total HBM: 20 TB.

Vera Rubin NVL72: Next-gen flagship with 72× Rubin GPUs. NVLink 6 at 3.6 TB/s plus ConnectX-9 NICs. Rack FP4 inference: 3,600 PFLOPS; HBM: 20.7 TB.

Advantage: Extreme single-rack compute density, mature CUDA ecosystem, and NVLink's absolute bandwidth lead keep NVIDIA ahead in per-cluster performance and developer convenience.

3. Measured Performance: Token Throughput Comparison

Under MLPerf Inference v6.1 (DeepSeek-R1, Offline, Closed Division) , system-level throughput differences are stark:

Total throughput (raw): GB300 NVL72 (288 GPUs) leads at 2,705,130 tokens/s ; Vera Rubin NVL72 (72 GPUs) at 1,183,327 tokens/s ; GB200 NVL72 at 2,091,190 tokens/s .

Per-GPU throughput (normalized): Vera Rubin NVL72 shows a staggering generational leap: 16,435 tokens/s per GPU , far above GB300 (9,393) and GB200 (7,261), proving Rubin's massive FP4 inference efficiency gain.

4. Conclusions & Trend Insights

Low-precision compute becomes the main battlefield: From A100 to Rubin, FP16 gains are modest, but FP8/FP4 sparse compute grows exponentially . NVIDIA Rubin hits 35 PFLOPS FP4 sparse; Huawei 950DT targets 2 PFLOPS FP4. Low-precision quantization is the core lever for inference throughput.

Interconnect bandwidth sets the cluster ceiling: Large-model training hits the communication wall. Huawei 950DT's 16 PB/s system interconnect and NVIDIA Rubin's 3.6 TB/s NVLink 6 both target multi-GPU coordination bottlenecks.

System-level delivery is the new normal: Both vendors have moved from selling chips to selling full-rack / super-node solutions . BOM complexity surges; direct liquid cooling (DLC) becomes standard.

Ecosystem vs. scale game: NVIDIA leads with closed-but-efficient CUDA and single-rack density; Huawei builds differentiated advantage in specific markets via ultra-large cluster scale (8,192 GPUs) and self-reliant UBoE/Lingqu ecosystem .

2026 marks a watershed for AI infrastructure. NVIDIA Rubin pushes single-GPU inference efficiency to new heights, while Huawei Ascend 950DT deployment signals maturity of domestic ultra-large clusters. For large-model training, HBM capacity, interconnect bandwidth, and system-scale expansion capability now far outweigh raw FP16 single-chip performance. Future competition will unfold between "closed but extremely efficient" and "open but massively scaled" paradigms.