NVIDIA Corporation

NVIDIA Corporation Earnings Recaps

NVDA Information Technology 2 recaps
Next earnings: November 18, 2026 (estimated) · full calendar
Q2 2027 Aug 28, 2026

NVIDIA's shares rose 7.0% following better-than-expected execution on robust AI-driven revenue growth and accelerating product ramps amid ongoing supply constraints.

Key takeaways
  • Q2 revenue hit a record $96 billion, more than doubling year-over-year, driven by accelerating AI demand and infrastructure buildout.
  • Data center revenue grew 18% sequentially to $89 billion, led by hyperscale (+13%) and ACIE (+25%) segments.
  • Supply constraints remain a limiting factor, with management targeting ~70% revenue growth in fiscal 2028 despite demand signaling potential to double.
  • Product ramps are notably strong: Vera Rubin platform shipments started early with multi-cloud customer adoption, expected to be NVIDIA’s fastest ramp.
  • Networking revenue surged 18% sequentially, with Spectrum-X Ethernet growing 2.6x year-over-year, supporting the broad AI infrastructure build.
Q4 2026 Feb 27, 2026

NVIDIA reported exceptional fourth-quarter results for fiscal 2026, achieving record revenue of $68 billion, reflecting a 73% year-over-year increase, driven primarily by the booming demand for data center solutions and AI applications.

Key takeaways
  • Data center revenue reached $62 billion in Q4, up 75% year-over-year and 22% sequentially, showcasing strong adoption of the Blackwell architecture.
  • NVIDIA's networking business saw a remarkable growth of over 10x since the acquisition of Mellanox, generating $31 billion in revenue for the year.
  • The demand for AI-powered solutions continues to reshape customer spending patterns, with major cloud providers increasing CapEx by nearly $120 billion in 2026.
  • Sovereign AI contracts tripled in fiscal 2026, with over $30 billion in revenue coming from government clients in multiple countries.
  • The company remains committed to innovation, with an R&D budget nearing $20 billion, focusing on maximizing AI performance and cost efficiency.