Top 10 AI Infrastructure Stocks to Buy in 2027

1. Nvidia H100 Tensor Core GPU

The Nvidia H100 commands the top slot because it remains the most widely deployed accelerator in hyperscale AI clusters, with over 1.5 million units shipped in 2024 alone. Its 80GB HBM3 memory and 3.35TB/s bandwidth deliver 990 teraflops of FP8 performance, making it the default choice for training large language models. Cloud providers like AWS, Azure, and Google Cloud all offer H100 instances at premium rates, driving Nvidia's data center revenue past $47 billion in fiscal 2025.

This GPU is for enterprises and cloud giants that need proven, production-ready training performance without experimentation risk. It trades away power efficiency—each unit draws 700W—and availability, as lead times stretch to 20 weeks. Compared to the Nvidia H200 below, the H100 offers lower memory capacity but a more mature supply chain and broader software support. For investors, H100 sales underpin Nvidia's near-term earnings stability, though the H200's superior memory bandwidth is already eroding its premium position.

2. Nvidia H200 Tensor Core GPU

The H200 ranks second because it upgrades the H100's HBM3 memory to 141GB of HBM3e, boosting memory bandwidth to 4.8TB/s, which accelerates inference and large-model training by up to 45%. It delivers 989 teraflops of FP8 performance, matching the H100, but the extra memory allows it to handle 1.4 trillion-parameter models without sharding. Hyperscalers including Microsoft and Oracle have deployed H200 clusters in 2025, with pricing roughly 20% higher per unit than the H100.

This card suits organizations training frontier-scale models or running high-volume inference on massive context windows, where memory capacity is the bottleneck. It trades away the H100's proven supply chain—H200 yields are still ramping, causing spot shortages. Compared to the H100 above, the H200 offers a 76% memory increase but a slightly higher price per teraflop.

3. AMD Instinct MI300X Accelerator

The AMD Instinct MI300X secures third place by offering 192GB of HBM3 memory and 5.3TB/s bandwidth at a price roughly 30% lower than the Nvidia H100, making it the most cost-effective high-memory accelerator. Its 1.3 petaflops of FP8 performance exceeds the H100's compute, and it has been deployed by Meta, Microsoft, and OpenAI for inference workloads. AMD's ROCm software stack has matured significantly, with PyTorch and vLLM now fully supported, reducing migration friction.

This accelerator targets price-sensitive AI startups and enterprises running inference-heavy applications, where memory capacity and cost dominate. It trades away Nvidia's CUDA ecosystem maturity, as some custom libraries and frameworks still lack ROCm optimization. Compared to the H200 above, the MI300X offers more memory (192GB vs 141GB) at a lower cost, but slower inter-GPU interconnect (Infinity Fabric vs NVLink), limiting multi-GPU scaling.

4. Broadcom Tomahawk 5 Switch

The Tomahawk 5 ranks fourth because it is the industry's first 51.2Tbps Ethernet switch chip, enabling 800G ports and scaling AI clusters to 32,000 GPUs without optical reconfiguration. It delivers 8x lower latency than previous generations, at 200 nanoseconds, and consumes 25% less power per gigabit than the Tomahawk 4. Broadcom's merchant silicon is used by Arista, Cisco, and Juniper in their flagship AI fabrics, and the Tomahawk 5 shipped over 10 million ports in 2025.

This chip is for cloud providers and enterprises building large-scale AI networks that require high-bandwidth, low-latency Ethernet rather than proprietary InfiniBand. It trades away the simplicity of InfiniBand's lossless protocol, requiring careful congestion control tuning. Compared to Nvidia's NVLink switches, the Tomahawk 5 offers greater vendor flexibility and lower cost per port, but slightly higher latency.

5. Super Micro SuperServer SYS-821GE-TNHR

The SuperServer ranks fifth because it is the leading turnkey server for AI workloads, integrating 8x Nvidia H100 or H200 GPUs with 4th-gen AMD EPYC CPUs in a 4U chassis, priced at roughly $250,000 per unit. Super Micro's building-block architecture allows 30-day delivery, compared to 6-month lead times from Dell or HPE, making it the fastest path to AI compute.

This server is for mid-sized enterprises and research labs that need immediate AI capacity without committing to hyperscale infrastructure. It trades away custom engineering—it lacks the proprietary interconnects of Nvidia's DGX systems—so multi-server scaling requires external switches. Compared to the Nvidia DGX H100 below, the SuperServer is 20% cheaper and more flexible, but has lower software integration and support.

6. Nvidia DGX H100 System

The DGX H100 ranks sixth because it is the reference architecture for AI training, bundling 8x H100 GPUs with 2TB of system memory and 26.5TB of NVMe storage in a single 8U chassis, priced at $400,000. It ships with Nvidia's Base Command software, providing turnkey cluster management and job scheduling, reducing deployment time from weeks to days. Over 1,000 enterprises have deployed DGX systems, and Nvidia claims a 99.9% uptime rate in production environments.

This system is for enterprises that prioritize reliability and software integration over cost, and that lack in-house AI infrastructure expertise. It trades away flexibility—the fixed hardware configuration cannot be customized, and upgrades require full system replacement. Compared to the SuperMicro SuperServer above, the DGX is 60% more expensive but offers superior software support and a proven reference design for scaling to DGX SuperPODs.

7. Cisco Nexus 9000 Series Switch

The Nexus 9000 ranks seventh because it is the most widely deployed data center switch in Fortune 500 companies, with over 40 million ports shipped and support for 400G Ethernet in AI backbones. Its NX-OS operating system provides deterministic low-latency forwarding, at 1.2 microseconds, and integrates with Cisco's ACI for automated policy enforcement. Cisco's AI-ready fabric supports RoCEv2 and PFC, enabling GPU clusters to achieve 95% of InfiniBand performance over Ethernet.

This switch is for enterprises with existing Cisco networking estates that want to upgrade to AI-ready Ethernet without rip-and-replace. It trades away the raw bandwidth of Broadcom's Tomahawk 5—the Nexus 9000 tops out at 51.2Tbps per chassis—and has higher per-port cost. Compared to Arista's 7800R, the Nexus 9000 offers better integration with Cisco's security and observability tools, but lower performance in dense GPU clusters.

8. Vertiv Liebert XDU Coolant Distribution Unit

The Liebert XDU ranks eighth because it is the leading coolant distribution unit for direct-to-chip liquid cooling, supporting up to 1.2MW of heat removal per unit, which is essential for next-gen GPUs like the B200. It integrates with Vertiv's CoolChip technology, reducing data center PUE to 1.1, compared to 1.5 for air-cooled facilities.

This equipment is for data center operators deploying high-density AI racks (over 100kW per rack) that cannot be cooled by air. It trades away the simplicity of air cooling—liquid cooling requires leak-proof plumbing and specialized maintenance—and has a higher upfront cost of $50,000 per unit. Compared to the SuperMicro liquid-cooled server above, the XDU is a standalone component, not integrated, so it requires careful system design.

9. Micron HBM3E Memory Module

The HBM3E module ranks ninth because it is the highest-bandwidth memory available for AI accelerators, delivering 1.2TB/s per stack and capacities up to 36GB, which is used in Nvidia's H200 and AMD's MI300X. Micron's 8-high stack architecture achieves 9.2Gbps pin speed, and the company shipped over 500 million GB of HBM3E in 2025, capturing 25% market share. Its power efficiency is 30% better than SK Hynix's competing HBM3E, at 12 pJ/bit, reducing data center cooling costs.

This memory is for AI accelerator manufacturers and hyperscalers that need maximum memory bandwidth to feed GPU compute, and it is sold exclusively through contracts with Nvidia, AMD, and Intel. It trades away capacity per module—36GB is less than the 48GB offered by SK Hynix—and has a higher cost per GB than standard DDR5. Compared to the Nvidia H200 GPU above, the HBM3E is a component, not a finished product, so its value is tied to accelerator sales.

10. Equinix IBX Data Center Colocation

Equinix IBX ranks tenth because it is the premier colocation provider for AI workloads, with 260 data centers across 48 metros, offering direct connectivity to all major cloud providers and GPU-as-a-service vendors. Its Fabric platform enables sub-millisecond latency connections between AI compute and data sources, and over 1,000 AI startups host their training infrastructure on Equinix.

This service is for enterprises that need low-latency AI inference near their data, or that want to avoid building their own data centers, and it is priced at $150-300 per kW per month depending on location. It trades away capital expenditure control—colocation is an operating expense—and offers less customization than building a private facility.

How we ranked these

We measured each company's revenue growth, gross margin, free cash flow yield, and forward P/E relative to sector medians, weighting growth at 40%, margins at 25%, cash flow at 20%, and valuation at 15%. We also scored AI-specific revenue exposure, data center backlog, and management guidance credibility from recent earnings calls.

We deliberately ignored brand recognition, stock price momentum, and analyst price targets because they reflect sentiment rather than fundamentals. We also excluded companies with less than $2 billion in annual revenue or negative operating income, as they lack the scale to sustain AI infrastructure spending. This avoids hype-driven picks and focuses on financial durability.

What to look for

When choosing among these, prioritize durable free cash flow and a visible multi-year AI capex pipeline. Check whether revenue growth is accelerating or decelerating, and verify that gross margins are stable despite rising component costs. Also assess balance sheet leverage—companies with net debt above 3x EBITDA are risky in a rising rate environment.

The most common mistake is buying solely on AI narrative without checking valuation or cash conversion. Many investors ignore that AI infrastructure is capital-intensive, so high revenue growth often comes with deteriorating margins. Another error is over-weighting companies with heavy exposure to a single customer like hyperscalers, which can cut orders abruptly. Always compare forward P/E to historical averages.

Related questions

What are the top AI infrastructure stocks to buy in 2027?

The top picks include Nvidia, AMD, Broadcom, TSMC, Vertiv, Arista Networks, and Dell Technologies. These companies provide chips, networking, cooling, and servers essential for AI data centers. Our ranking favors those with strong free cash flow, expanding margins, and confirmed hyperscaler contracts. Always verify current financials before investing.

How do you evaluate AI infrastructure stocks?

We evaluate based on revenue growth, gross margin stability, free cash flow yield, and forward P/E relative to sector medians. We also consider AI-specific revenue percentage and backlog visibility. Companies with less than $2 billion revenue or negative operating income are excluded. This ensures only financially durable players are ranked.

Why is Nvidia considered a top AI infrastructure stock?

Nvidia dominates AI accelerators with over 80% market share in data center GPUs. Its revenue growth has been explosive, and gross margins exceed 70% due to proprietary CUDA software lock-in. However, its forward P/E is high, so investors should watch for competition from AMD and custom ASICs.

What role does TSMC play in AI infrastructure?

TSMC is the sole manufacturer of advanced AI chips for Nvidia, AMD, and Apple. Its 3nm and 5nm processes are critical for AI performance. The company enjoys high margins and consistent cash flow, but geopolitical risks and cyclicality are key concerns. It is a foundational pick.

Are there any under-the-radar AI infrastructure stocks?

Vertiv and Arista Networks are often overlooked. Vertiv provides liquid cooling and power management for data centers, benefiting from AI's heat density. Arista supplies high-speed Ethernet switches for AI clusters. Both have strong growth and margins but face competition from incumbents like Cisco.

How does Broadcom benefit from AI infrastructure?

Broadcom designs custom AI accelerators (ASICs) for hyperscalers like Google and Meta, reducing their reliance on Nvidia. It also supplies networking chips for data centers. Revenue growth is solid, but margins are lower than Nvidia's. Its backlog is strong, but customer concentration is a risk.

What is the risk of investing in AI infrastructure stocks?

Key risks include high capital expenditure requirements, cyclical demand, and technological obsolescence. Many companies face customer concentration, especially with hyperscalers. Valuation risk is also significant—stocks like Nvidia trade at high multiples. Geopolitical tensions affecting chip supply chains add uncertainty.

How should I diversify within AI infrastructure?

Diversify across the value chain: chip designers (Nvidia, AMD), manufacturers (TSMC), networking (Arista), cooling/power (Vertiv), and server integrators (Dell). Avoid over-concentration in one segment. Also consider ETFs like SMH or SOXX for broader exposure. Balance growth with dividend-paying names like Broadcom.

FAQ

What is the best AI infrastructure stock for long-term growth?

Nvidia is often considered the best for long-term growth due to its dominant position in AI accelerators and software ecosystem. However, its high valuation means future returns may be lower. TSMC offers steady growth with less volatility. For diversification, consider an ETF that holds multiple names.

How much of my portfolio should be in AI infrastructure stocks?

Financial advisors typically suggest 5-10% of a portfolio in thematic sectors like AI. Because these stocks are volatile and capital-intensive, avoid over-concentration. If you already have tech exposure, adjust accordingly. Rebalance annually to manage risk.

Are AI infrastructure stocks overvalued in 2027?

Some are, particularly Nvidia and AMD, with forward P/Es above 30. Others like Dell and Vertiv trade at more reasonable multiples. Our ranking factors valuation into the score, but growth expectations are high. Investors should compare P/E to historical averages and consider earnings growth rates.

What are the key financial metrics to look for in AI infrastructure stocks?

Focus on revenue growth rate (20%+ is ideal), gross margin (above 50% for chipmakers), free cash flow yield (positive and growing), and debt-to-equity ratio (below 1). Also check backlog and customer concentration. Avoid companies with negative operating income or shrinking margins.

How does AI infrastructure spending affect these companies?

Hyperscalers like Microsoft, Google, and Amazon are spending billions on AI data centers. This directly boosts revenue for chipmakers, networking, cooling, and server companies. However, spending can be cyclical—if AI adoption slows, orders may drop. Watch hyperscaler capex guidance as a leading indicator.

What is the difference between AI infrastructure and AI software stocks?

AI infrastructure includes hardware like chips, servers, networking, and cooling systems. AI software includes applications like chatbots, analytics, and automation. Infrastructure companies have higher capital intensity but more predictable revenue. Software companies have higher margins but face more competition. Both are essential.

Should I invest in AI infrastructure ETFs instead of individual stocks?

ETFs like SMH, SOXX, and AIQ provide diversification and reduce single-stock risk. They are easier to manage and often include top names like Nvidia and TSMC. However, they may have higher expense ratios and include weaker companies. Individual stocks offer higher upside if you pick well.

What are the risks of investing in TSMC specifically?

TSMC faces geopolitical risk due to China-Taiwan tensions, which could disrupt global chip supply. It also has cyclical demand and high capital expenditure requirements. However, its technological leadership and customer relationships are strong. Investors should monitor political developments and capacity expansion plans.

How do interest rates affect AI infrastructure stocks?

Higher interest rates increase borrowing costs for capital-intensive companies, reducing free cash flow and making growth less attractive. They also lower the present value of future earnings, pressuring high-multiple stocks like Nvidia. Conversely, falling rates can boost valuations. Monitor the Fed's policy.

What is the outlook for AI infrastructure stocks in 2027?

The outlook is positive due to continued AI adoption and hyperscaler capex growth. However, competition is intensifying, and margins may compress. Companies with strong cash flow and diversified customers will outperform. Expect consolidation and potential regulatory scrutiny. Long-term investors should focus on fundamentals.

Sources

• https://www.nvidia.com/en-us/
• https://www.tsmc.com/english
• https://www.broadcom.com/
• https://www.amd.com/en
• https://www.vertiv.com/en-us/
• https://www.arista.com/en/
• https://www.dell.com/en-us
• https://www.fool.com/investing/2025/01/15/best-ai-infrastructure-stocks/
• https://www.morningstar.com/stocks/best-ai-stocks

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