Artificial intelligence will be the driving force behind the next wave of technological innovation. From powering generative AI to accelerating machine learning applications, the demand for advanced hardware has never been greater.
Publicly traded AI hardware companies are emerging as one of the market’s most attractive investment opportunities for forward-looking investors. For investors looking to gain exposure to this high-growth sector, these companies stand out not only for their cutting-edge technologies but also for their long-term market potential.
Here are 10 of the top AI hardware stocks to buy today. Backed by strong institutional coverage and growing global demand, these stocks offer investors exposure to the explosive growth of artificial intelligence.
| Company Name (Ticker) | Analyst Upside | P/E Ratio |
| Qualcomm (QCOM) | 14.6% | 12.97 |
| Marvell Technology (MRVL) | 27.9% | 24.03 |
| NVIDIA (NVDA) | 9.4% | 35.29 |
| AMD (AMD) | 12.9% | 32.34 |
| Intel (INTC) | -6.6% | 102.98 |
| TSMC (TSM) | 18.7% | 22.71 |
| Broadcom (AVGO) | 3.0% | 39.69 |
| Micron Technology (MU) | 29.3% | 9.65 |
| Super Micro Computer (SMCI) | 18.3% | 16.21 |
| Arista Networks (ANET) | 5.9% | 44.39 |
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These are 3 of the top AI hardware stocks that could be the most undervalued today.
AMD (AMD)
AMD is the most credible challenger to NVIDIA in data-center AI, with a fast-moving accelerator roadmap and growing cloud adoption. Microsoft Azure made MI300X clusters generally available, giving customers a mainstream alternative to NVIDIA for training and inference, and AMD has been explicit about a rapid cadence: MI325X shipments began after Q4 2024, and the CDNA 4–based MI350 series is slated for 2025 with sizable inference gains versus MI300 (AMD’s claim).
That matters because inference is exploding as deployed models scale, and memory capacity/bandwidth (where MI3xx has emphasized leadership HBM capacity) can be as decisive as raw FLOPS. Strategically, AMD is winning design-ins across major hyperscalers and model labs while pushing an “open AI” pitch around ROCm and standards to reduce switching costs.
Management has repeatedly telegraphed tight AI-GPU supply through 2025 and rising datacenter mix; Reuters and company guidance have chronicled those step-ups alongside expectations that MI3xx revenue would ramp materially as capacity frees up. For investors, AMD’s thesis is a classic share-gain story in a market expanding so fast that the No. 2 vendor can still compound at high rates, provided it sustains execution on silicon, software, and supply (with TSMC as a key dependency).
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Taiwan Semiconductor Manufacturing (TSM)
If NVIDIA and AMD are selling picks, TSMC is the mine. It manufactures the vast majority of advanced AI accelerators and is in the middle of a once-in-a-generation capex and packaging build-out to keep up with demand. The company reported record results this summer, citing AI and HPC as the primary drivers; it also lifted its full-year growth outlook and confirmed that 2-nanometer (N2) mass production begins in late 2025 with revenue impact into 2026.
In parallel, TSMC is doubling and then further expanding CoWoS advanced packaging capacity critical for HBM-rich, chiplet-based AI parts, while adding new packaging sites in Taiwan and evaluating capacity in Japan. Why this belongs in an “AI hardware to buy” list: every incremental dollar of hyperscaler AI capex tends to flow through TSMC’s advanced nodes and packaging lines, regardless of whether the chip is NVIDIA, AMD, custom silicon, or a startup.
The company’s Q2 2025 print and monthly updates underscore broad-based AI strength, while its roadmap (A16 after N2) keeps performance-per-watt improvements on schedule for HPC/AI customers. Investors get diversified exposure to AI compute growth with less product-cycle risk than a single chip vendor, though currency swings, geopolitics, and overseas cost structures remain watch items; management continues to flag.
NVIDIA (NVDA)
NVIDIA is the purest play on AI compute. Its Blackwell platform (GB200) knits together 72 GPUs and 36 Grace CPUs into a single NVLink domain at rack scale (NVL72), which NVIDIA says delivers 30× faster real-time inference on trillion-parameter LLMs versus the prior generation, exactly the type of performance hyperscalers are buying by the acre.
Just as importantly, NVIDIA’s fifth-gen NVLink and NVLink Switch push cluster bandwidth to extreme levels, helping remove communication bottlenecks that slow training and inference at scale. That tight coupling of silicon, networking, and software is what keeps utilization high and total cost of ownership (TCO) attractive for cloud operators. The real moat, though, is the full-stack ecosystem, CUDA, libraries, frameworks, and system designs that have compounded for over a decade.
Even as NVIDIA opens NVLink Fusion to let partners build semi-custom AI silicon and pair third-party CPUs with its GPUs, the company remains the de-facto standard with 80% share of AI accelerators, according to industry reporting and surveys. Advanced packaging is another pillar: NVIDIA’s shift toward TSMC’s CoWoS-L for Blackwell underscores how it orchestrates scarce supply in the highest value nodes and packages.
NVIDIA still sets the cadence for data-center AI, and even its “openness” moves pull more developers and systems into its gravity well.
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Disclaimer:
Please note that the articles on TIKR are not intended to serve as investment or financial advice from TIKR or our content team, nor are they recommendations to buy or sell any stocks. We create our content based on TIKR Terminal’s investment data and analysts’ estimates. Our analysis might not include recent company news or important updates. TIKR has no position in any stocks mentioned. Thank you for reading, and happy investing!