Nvidia Stock Hasn't Been This Cheap in 7 Years. Is This the Ultimate Buying Opportunity?

Nvidia reported fiscal 2026 revenue of $215.9 billion, up 65% year over year, with data center revenue rising 68% to $193.7 billion; Q1 fiscal 2027 revenue accelerated to $81.6 billion, up 85%, and data center revenue jumped 92% to $75.2 billion. The article highlights the upcoming Vera Rubin launch, which Nvidia says can cut GPU needs by 75% for training and reduce inference token costs by up to 90%, while consensus fiscal 2027 GAAP EPS is projected to rise 91% to $9.36. Despite the stock's 13-fold gain over three years, its P/E fell to 30.09, near the lowest since 2019 and well below its seven-year average of 71.2, implying further upside if earnings continue to compound.

Analysis

NVDA’s setup is less about a single product launch and more about a compounding “platform tax” on the AI stack. If Rubin materially lowers $/token, the near-term winner is not just model labs but the hyperscalers and inference-heavy software vendors that can now justify more usage, which expands the addressable GPU pool rather than cannibalizing it. That creates a second-order loop: cheaper inference increases product adoption, which increases utilization, which tightens supply again and preserves pricing power.

The market may be underestimating how little elasticity there is in the current supply chain. When one vendor controls the full stack from GPU to networking to CPU, the bottleneck shifts from chip demand to data center power, interconnect, and rack-level deployment timelines; that favors NVDA’s attach rate and pressures smaller ASIC players whose value proposition depends on narrow workloads. The more important competitive risk is not a rival GPU on paper, but procurement customers diversifying for bargaining leverage if lead times compress in 2H, which could cap multiple expansion before it hurts unit growth.

The biggest tail risk is timing: the valuation case assumes earnings keep compounding into the next two quarters, but any slip in launch cadence, qualification, or customer capex gating would hit a name trading on perfection-like expectations despite the lower headline multiple. The contrarian read is that the “cheap” P/E is partly a mirage created by near-term earnings power at peak scarcity; if supply normalizes faster than demand reaccelerates, margin mix could peak before the stock does. Still, the asymmetry remains favorable as long as frontier AI budgets stay expansionary and inference economics keep improving, because every step down in cost expands the number of viable workloads, not just the volume of existing ones.

Key Decisions for Investors

• Stay long NVDA into the Rubin launch window, but size it as a momentum-plus-fundamentals trade, not a valuation anchor; use a 6-9 month horizon and trail stops if the market starts pricing in supply normalization faster than earnings upside.
• Pair long NVDA vs. short an AI infrastructure laggard basket (e.g., AMD/AVGO relative underperformers on AI exposure) for 3-6 months to isolate execution and platform-share capture rather than broad AI beta.
• Buy NVDA call spreads 3-6 months out to express upside into launch and first-order guidance reacceleration; prefer spreads over outright calls because the key risk is multiple compression if the event is well telegraphed.
• Add exposure to hyperscaler beneficiaries with rising AI utilization economics (MSFT, AMZN, GOOGL) on any post-launch pullback; Rubin’s lower inference costs should show up first in cloud demand before it is fully reflected in chip mix.
• Monitor for signs of customer diversification or capex pacing issues; if lead times shorten materially, trim 20-30% of NVDA exposure and rotate into suppliers with less valuation sensitivity to launch expectations.