Large AI clusters need fast, reliable, scale-out networking, and Arista is a leader there.
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Marvell sits in several AI choke points at once: custom accelerators, optical DSPs, interconnect, and scale-up silicon.
Astera helps solve rack-scale bottlenecks around PCIe, CXL, fabric connectivity, and memory movement.
AI clusters require dense high-speed interconnect, connectors, and copper cabling.
| Metric | ANET | MRVL | ALAB | APH |
|---|---|---|---|---|
| Price | $188.67 | $218.72 | $334.17 | $169.18 |
| 1D Change | -1.90% | +3.89% | +0.81% | -0.84% |
| Market Cap | $237.96B | $196.31B | $57.97B | $208.60B |
| Enterprise Value | $224.61B | $192.77B | $56.76B | $222.12B |
| Trailing P/E | 59.9 | 75.2 | 163.8 | 42.4 |
| Forward P/E | 36.7 | 35.0 | 53.4 | 26.2 |
| PEG Ratio | 2.52x | 1.19x | — | 1.20x |
| Price / Sales | 22.6 | 22.5 | 48.2 | 7.2 |
| EV / Revenue | 21.3 | 22.1 | 47.2 | 7.7 |
| Revenue Growth | 37.7% | 27.6% | 104.5% | 55.0% |
| Earnings Growth | 35.7% | -80.4% | 186.2% | 59.3% |
| Gross Margin | 63.0% | 51.5% | 75.1% | 39.0% |
| Operating Margin | 45.4% | 14.5% | 22.7% | 29.8% |
| Net Margin | 38.4% | 29.0% | 30.7% | 17.7% |
| ROE | 31.5% | 16.0% | 25.8% | 38.1% |
| Free Cash Flow | $3.89B | $2.27B | $91.9M | $3.82B |
| FCF Margin | 36.9% | 26.0% | 7.6% | 13.2% |
| Debt / Equity | — | 0.29x | 2.57x | 1.20x |
| Current Ratio | 2.96x | 3.28x | 0.10x | 1.89x |
| Dividend Yield | 0.00% | 0.11% | 0.00% | 0.59% |
| Next Earnings | Nov 03, 2026 | Aug 27, 2026 | Nov 03, 2026 | Oct 28, 2026 |
| Quarterly Revenue | $3.04B | $2.42B | $392.4M | $nan |
| Revenue QoQ | +12.1% | +9.0% | +27.3% | nan% |
| Quarterly Net Income | $1.21B | $34.5M | $153.1M | $nan |
| Net Income QoQ | +18.6% | -91.3% | +90.6% | nan% |
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.
MRVL thesis lens
Custom AI silicon + optical interconnect
Why it could benefit
- Marvell sits in several AI choke points at once: custom accelerators, optical DSPs, interconnect, and scale-up silicon.
- Hyperscalers building custom AI systems need merchant partners that can help on both compute-adjacent silicon and connectivity.
- That gives Marvell exposure to the parts of the AI factory that keep getting more complex as clusters scale.
Moat / edge
- Deep hyperscaler and OEM relationships in complex infrastructure silicon.
- A differentiated portfolio spanning custom silicon, networking, and optical components.
- Engineering credibility in performance-sensitive infrastructure markets.
What to watch
- Custom AI program ramps and customer concentration.
- Optical interconnect demand versus electrical alternatives.
- Gross-margin durability as AI mix grows.
Key risks
- Large design wins can be lumpy and take time to ramp.
- Execution matters when the thesis depends on several advanced product categories at once.
ALAB thesis lens
Rack-scale AI connectivity
Why it could benefit
- Astera helps solve rack-scale bottlenecks around PCIe, CXL, fabric connectivity, and memory movement.
- As AI systems move from single boxes to tightly linked racks, connectivity and orchestration become more valuable.
- It is one of the cleanest public ways to own the plumbing inside modern AI servers and racks.
Moat / edge
- Focused product portfolio aimed at specific AI system bottlenecks.
- Strong alignment with next-generation rack and accelerator architectures.
- Technical positioning in a market where performance and validation matter a lot.
What to watch
- Design-win conversion into production revenue.
- Customer concentration and platform transitions.
- Adoption of CXL and other rack-scale standards.
Key risks
- A younger company can see sharper swings as programs ramp.
- If key customer platforms slip, near-term growth can look worse quickly.
APH thesis lens
High-speed connectors and cabling
Why it could benefit
- AI clusters require dense high-speed interconnect, connectors, and copper cabling.
- Amphenol is a broad supplier into data center, communications, auto, and industrial markets.
- Rack-scale AI designs can raise content per server and per networking platform.
Moat / edge
- Broad connector and sensor portfolio.
- Deep customer relationships across high-reliability markets.
- Scale and acquisition discipline.
What to watch
- AI data-center organic growth.
- Margins after acquisitions.
- Telecom, industrial, and auto cycle recovery.
Key risks
- A diversified portfolio can dilute pure AI exposure.
- Connector demand can be cyclical.