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Nvidia reported $46.7 billion in revenue for fiscal Q2 2026 in their earnings announcement and call yesterday, with data center revenue hitting $41.1 billion, up 56% year over year. The company also released guidance for Q3, predicting a $54 billion quarter.
Behind these confirmed earnings call numbers lies a more complex story of how custom application-specific integrated circuits (ASICs) are gaining ground in key Nvidia segments and will challenge their growth in the quarters to come.
Bank of America’s Vivek Arya asked Nvidia’s president and CEO, Jensen Huang, if he saw any scenario where ASICs could take market share from Nvidia GPUs. ASICs continue to gain ground on performance and cost advantages over Nvidia, Broadcom projects 55% to 60% AI revenue growth next year.
Huang pushed back hard on the earnings call. He emphasized that building AI infrastructure is “really hard” and most ASIC projects fail to reach production. That’s a fair point, but they have a competitor in Broadcom, which is seeing its AI revenue steadily ramp up, approaching a $20 billion annual run rate. Further underscoring the growing competitive fragmentation of the market is how Google, Meta and Microsoft all deploy custom silicon at scale. The market has spoken.
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ASICs are redefining the competitive landscape in real-time
Nvidia is more than capable of competing with new ASIC providers. Where they’re running into headwinds is how effectively ASIC competitors are positioning the combination of their use cases, performance claims and cost positions. They’re also looking to differentiate themselves in terms of the level of ecosystem lock-in they require, with Broadcom leading in this competitive dimension.
The following table compares Nvidia Blackwell with its primary competitors. Real-world results vary significantly depending on specific workloads and deployment configurations:
MetricNvidia BlackwellGoogle TPU v5e/v6AWS Trainium/Inferentia2Intel Gaudi2/3Broadcom Jericho3-AIPrimary Use CasesTraining, inference, generative AIHyperscale training & inferenceAWS-focused training & inferenceTraining, inference, hybrid-cloud deploymentsAI cluster networkingPerformance ClaimsUp 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 PositionPremium pricing, comprehensive ecosystemSignificant savings vs GPUs per Google*Aggressive pricing per AWS marketing*Budget alternat …