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Nvidia’s Impressive Financial Performance
Nvidia reported an impressive $46.7 billion in revenue for fiscal Q2 2026 during their recent earnings announcement and call. Notably, data center revenue soared to $41.1 billion, reflecting a remarkable 56% increase year over year. The company also provided guidance for Q3, forecasting a revenue of $54 billion. However, beneath these strong earnings figures lies a more intricate narrative regarding the growing influence of custom application-specific integrated circuits (ASICs) in key Nvidia segments, which may pose challenges to their growth in the upcoming quarters.
Competitive Landscape and Challenges
During the earnings call, Bank of America’s Vivek Arya inquired whether Nvidia’s president and CEO, Jensen Huang, perceived any scenarios in which ASICs could potentially capture market share from Nvidia’s GPUs. ASICs are increasingly recognized for their performance and cost advantages, with Broadcom predicting a 55% to 60% growth in AI revenue next year. Huang firmly countered this notion during the call, asserting that developing AI infrastructure is “really hard” and that most ASIC projects fail to reach production. While this is a valid point, Broadcom remains a formidable competitor, with its AI revenue steadily approaching a $20 billion annual run rate.
The competitive fragmentation of the market is further highlighted by the fact that major players like Google, Meta, and Microsoft are deploying custom silicon at scale, signaling a shift in the landscape.
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Nvidia’s Competitive Edge
Nvidia is certainly equipped to compete with emerging ASIC providers. However, the company faces challenges in effectively countering how these competitors position their use cases, performance claims, and pricing strategies. They are also differentiating themselves based on the level of ecosystem lock-in required, with Broadcom leading in this regard.
The following table compares Nvidia’s Blackwell with its main competitors, illustrating that real-world results can vary significantly based on specific workloads and deployment configurations:
| Metric | Nvidia Blackwell | Google TPU v5e/v6 | AWS Trainium/Inferentia2 | Intel Gaudi2/3 | Broadcom Jericho3-AI |
|——–|——————|——————-|—————————|—————-|———————-|
| Primary Use Cases | Training, inference, generative AI | Hyperscale training & inference | AWS-focused training & inference | Training, inference, hybrid-cloud deployments | AI cluster networking |
| Performance Claims | Up to 50x improvement over Hopper* | 67% improvement TPU v6 vs v5* | Comparable GPU performance at lower power* | 2-4x price-performance vs prior gen* | InfiniBand parity on Ethernet* |
| Cost Position | Premium pricing, comprehensive ecosystem | Significant savings vs GPUs per Google* | Aggressive pricing per AWS marketing* | Budget alternative positioning* | Lower networking TCO per vendor* |
| Ecosystem Lock-In | Moderate (CUDA, proprietary) | High (Google Cloud, TensorFlow/JAX) | High (AWS, proprietary Neuron SDK) | Moderate (supports open stack) | Low (Ethernet-based standards) |
| Availability | Universal (cloud, OEM) | Google Cloud-exclusive | AWS-exclusive | Multiple cloud and on-premise | Broadcom direct, OEM integrators |
| Strategic Appeal | Proven scale, broad support | Cloud workload optimization | AWS integration advantages | Multi-cloud flexibility | Simplified networking |
| Market Position | Leadership with margin pressure | Growing in specific workloads | Expanding within AWS | Emerging alternative | Infrastructure enabler |
*Performance-per-watt improvements and cost savings depend on specific workload characteristics, model types, deployment configurations, and vendor testing assumptions. Actual results vary significantly by use case.
The Shift Towards Custom Silicon
Every major cloud provider has embraced custom silicon to achieve performance, cost efficiency, ecosystem scale, and extensive DevOps benefits associated with developing ASICs from the ground up. Google operates TPU v6 in production through its partnership with Broadcom, while Meta has created MTIA chips specifically for ranking and recommendations. Microsoft is developing Project Maia for sustainable AI workloads, and Amazon Web Services promotes the use of Trainium for training and Inferentia for inference.
Additionally, ByteDance operates TikTok recommendations on custom silicon, despite geopolitical tensions, resulting in billions of inference requests processed daily on ASICs rather than GPUs. CFO Colette Kress acknowledged this competitive reality during the earnings call, noting that revenue from China had decreased to a low single-digit percentage of data center revenue. Current Q3 guidance completely excludes H20 shipments to China. While Huang attempted to highlight China’s extensive opportunities during the earnings call, it was evident that equity analysts were skeptical of his optimistic outlook.