NVIDIA remains the center of AI training and high-end inference demand.
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Broadcom benefits from AI through custom accelerators, networking chips, and high-speed connectivity.
Most leading AI chips still depend on TSMC's manufacturing and advanced packaging.
Large AI clusters need fast, reliable, scale-out networking, and Arista is a leader there.
| Metric | NVDA | AVGO | TSM | ANET |
|---|---|---|---|---|
| Price | $215.95 | $429.34 | $413.20 | $138.86 |
| 1D Change | +1.89% | +3.84% | -0.43% | -2.15% |
| Market Cap | $5.25T | $2.03T | $2.14T | $174.85B |
| Enterprise Value | $5.09T | $2.01T | $8.41T | $166.14B |
| Trailing P/E | 44.0 | 83.9 | 35.5 | 47.7 |
| Forward P/E | 19.1 | 23.7 | 21.4 | 31.4 |
| Price / Sales | 24.3 | 29.8 | 0.5 | 18.0 |
| EV / Revenue | 23.6 | 29.4 | 2.0 | 17.1 |
| Revenue Growth | 73.2% | 29.5% | 35.1% | 35.1% |
| Earnings Growth | 95.6% | 31.6% | 58.4% | 25.0% |
| Gross Margin | 71.1% | 76.7% | 61.9% | 63.5% |
| Operating Margin | 65.0% | 44.9% | 58.1% | 42.7% |
| Net Margin | 55.6% | 36.6% | 46.5% | 38.3% |
| ROE | 101.5% | 33.4% | 36.2% | 31.5% |
| Free Cash Flow | $58.13B | $25.50B | $721.56B | $4.36B |
| FCF Margin | 26.9% | 37.4% | 17.6% | 44.9% |
| Debt / Equity | 7.25x | 0.83x | 0.17x | — |
| Current Ratio | 3.90x | 1.90x | 2.49x | 2.83x |
| Dividend Yield | 2.00% | 63.00% | 85.00% | — |
| Next Earnings | May 20, 2026 | Jun 03, 2026 | Jul 16, 2026 | Aug 04, 2026 |
| Quarterly Revenue | $68.13B | $19.31B | $nan | $2.49B |
| Revenue QoQ | +19.5% | +7.2% | nan% | +7.8% |
| Quarterly Net Income | $42.96B | $7.35B | $nan | $955.8M |
| Net Income QoQ | +34.6% | -13.7% | nan% | +12.1% |
NVDA thesis lens
AI compute platform
Why it could benefit
- NVIDIA remains the center of AI training and high-end inference demand.
- Its stack includes chips, networking, systems, CUDA, and software libraries, not just GPUs.
- As models get larger and enterprises move into production, full-stack control becomes more valuable.
Moat / edge
- CUDA ecosystem and developer lock-in.
- Leading performance in accelerated computing.
- Integrated platform spanning silicon, interconnect, and software.
What to watch
- Supply-demand balance for each new architecture cycle.
- Mix shift between hyperscalers and enterprise customers.
- Competition from custom silicon and AMD.
Key risks
- Customer concentration and product-transition execution matter a lot.
- Any sharp slowdown in capex could compress expectations quickly.
AVGO thesis lens
Custom AI silicon + networking
Why it could benefit
- Broadcom benefits from AI through custom accelerators, networking chips, and high-speed connectivity.
- Hyperscalers want alternatives to one-size-fits-all GPU designs, and Broadcom helps build them.
- Its infrastructure-software cash flows can support long-term investment while reducing single-cycle risk.
Moat / edge
- Deep engineering relationships with hyperscalers and OEMs.
- Mission-critical connectivity in high-performance systems.
- Diversified portfolio across semis and infrastructure software.
What to watch
- Custom ASIC program wins and revenue ramps.
- Networking demand tied to scale-out AI clusters.
- Integration and returns from software assets.
Key risks
- Large customers can have lumpy spending patterns.
- Execution risk rises as custom programs scale in complexity.
TSM thesis lens
Advanced semiconductor manufacturing
Why it could benefit
- Most leading AI chips still depend on TSMC's manufacturing and advanced packaging.
- As AI complexity rises, foundry leadership in yield, scale, and packaging matters more.
- TSMC is a cleaner way to own the ecosystem rather than one end-market winner.
Moat / edge
- Leading-edge process technology.
- Manufacturing scale and execution track record.
- Hard-to-replicate ecosystem trust with top chip designers.
What to watch
- Capacity additions in advanced nodes and CoWoS-type packaging.
- Geographic expansion and margin preservation.
- Mix between smartphone, HPC, and AI demand.
Key risks
- Geopolitical risk is always part of the TSMC thesis.
- Large customer concentration can amplify cycle swings.
ANET thesis lens
AI data-center networking
Why it could benefit
- Large AI clusters need fast, reliable, scale-out networking, and Arista is a leader there.
- Ethernet's role in AI data centers keeps growing as architectures evolve.
- Arista is leveraged to both hyperscaler and enterprise data-center modernization.
Moat / edge
- Strong software layer and operational simplicity.
- Trusted relationships with sophisticated cloud customers.
- High-performance Ethernet expertise.
What to watch
- AI-cluster networking mix versus traditional cloud networking.
- Customer concentration and spending cadence.
- Competition from incumbents and custom architectures.
Key risks
- Large orders can be lumpy quarter to quarter.
- If architecture choices shift, product mix could change quickly.