For the past couple of years, one of the more dominant conversations in AI investing has been about training. Which company makes the best chips for training large language models? Which hyperscaler has the most GPU capacity? Those were the right questions for the previous several years. But in 2026, the conversation is quietly shifting, and investors who recognize it early might be rewarded down the line.
The new phase and key term is inference. Every time someone uses ChatGPT, asks Google a question powered by Gemini, or runs an AI agent inside an enterprise application, a model is executing a real-time response. That is inference, and it happens billions of times per day at a scale now beginning to dwarf total training workloads in terms of compute demand.
The infrastructure required to run inference efficiently, at low latency, at massive scale, and at a cost that makes commercial deployment viable is fundamentally different from that for training. And the companies best positioned to supply it potentially represent one of the most compelling investment themes in the market right now. Here are five of them.
NVIDIA: Doubling Down on Inference
NVIDIA (NASDAQ: NVDA) has built a clear dominance in AI training. But the company has moved decisively to ensure it owns the inference era too. CEO Jensen Huang has been explicit that inference is now the fastest-growing AI workload, and NVIDIA's Blackwell GPU architecture was designed with inference performance as its primary objective. The company's fiscal year 2026 (FY2026) revenue reached $215.94 billion, up 65% year over year, with earnings of $120.07 billion. Those are numbers that reflect a business still in a steep growth curve.
The most forward-looking signal was the late-2025 announced acquisition of Groq, an inference-focused silicon startup founded by former Google TPU architects, valued at $20 billion. Groq developed chips specifically optimized for inference speed and efficiency. By integrating that capability alongside NVIDIA's existing inference software stack, Jensen Huang is ensuring that NVIDIA captures the inference opportunity as comprehensively as it captured the training opportunity.
Q1 FY2027 earnings are due May 20, and with the stock trading near its 52-week and all-time high, that report will be as closely watched as any. Analysts hold a consensus Buy rating across 54 ratings, with a price target of $275.25, implying a hugely impressive 25% upside potential.
Alphabet: Processing 16 Billion Inference Tokens Per Minute
Alphabet (NASDAQ: GOOGL) is running inference at a scale that is genuinely difficult to comprehend. Google's first-party models are now processing 16 billion tokens per minute, up 60% quarter over quarter.
Every Google Search enhanced by AI, every Gemini response, every AI Overview is inference in action, and the volume is accelerating. The company's Q1 2026 results made that tangible. Google Cloud revenue hit $20.03 billion, up 63% year over year, with the cloud backlog nearly doubling quarter over quarter to over $460 billion.
What makes Alphabet particularly compelling in the inference context is its proprietary silicon. Google's 8th-generation TPU 8i was built specifically for inference, delivering near-zero latency at a cost per token that is structurally lower than GPU-based inference at scale. That cost advantage matters enormously as inference volume compounds. At a forward P/E of 27, Alphabet remains one of the more reasonably valued mega-cap technology companies despite being one of the biggest outperformers over the prior year. The consensus among 54 analysts is Moderate Buy, with a price target of $407.86, forecasting modest upside potential.
Amazon: Building the Inference Platform the World Will Run On
Amazon (NASDAQ: AMZN) is building the inference infrastructure that millions of businesses will use to run AI in production. AWS processed more tokens through its Bedrock managed inference platform in Q1 2026 alone than in all prior years combined. That is not just a gradual inflection, but a total step change.
AWS grew 28% year over year to $37.6 billion in Q1, its fastest growth rate in 15 quarters, driven almost entirely by AI workloads where inference is rapidly becoming the dominant cost category.
The Trainium chip program is Amazon's proprietary play on inference economics. The chips business, comprising Trainium, Graviton, and Nitro, has crossed a $20 billion annualized revenue run rate, growing at triple-digit year-over-year rates. Amazon is not just a customer of inference infrastructure; it is becoming one of its most important suppliers. Q2 2026 guidance of $194 billion to $199 billion points to continued momentum. The stock is up close to 20% year to date, and analysts hold a consensus Moderate Buy rating with a price target forecasting almost 15% upside potential.
Credo Technology: The Interconnect Specialist at the Heart of Inference Clusters
Credo Technology (NASDAQ: CRDO) is a name many retail investors might not yet have encountered, but institutional investors have been piling in. Over the prior 12 months, institutional inflows into this almost $35 billion company have totaled almost $6.6 billion, versus $3.3 billion in outflows. The company designs high-speed active electrical cables, optical transceivers, and connectivity solutions that eliminate the bandwidth and latency bottlenecks inside AI inference clusters. Its ZeroFlap AECs and OmniConnect memory platform are embedded in the hyperscale data center infrastructure being built at unprecedented scale by the world's largest technology companies.
The fundamental numbers have been impressive to say the least. Fiscal Q3 2026 revenue came in at $407 million, up 201.5% year over year, with earnings per share of $1.07, crushing the 78-cent consensus estimate. Full-year fiscal 2026 revenue is now tracking above $1.3 billion. The recently completed DustPhotonics acquisition brings Silicon Photonics PIC technology in-house, targeting over $500 million in optical revenue in fiscal 2027 alone and positioning Credo as a vertically integrated AI connectivity leader across both electrical and optical interconnects.
In total, 19 analysts cover the stock, with a consensus Moderate Buy rating and a target of $207.71, suggesting close to 10% upside. And while the analyst target may not seem impressive at first, it’s worth noting that the stock has surged about 280% over the prior 12 months. In that regard, it’s significant that analysts still see additional near-term upside for the stock.
Astera Labs: Pure-Play Inference Connectivity in a High-Growth Phase
Astera Labs (NASDAQ: ALAB) might be one of the most targeted plays on the inference boom among the five names on this list. The company designs semiconductor-based connectivity solutions that eliminate bandwidth and latency bottlenecks in AI data centers. Its PCIe 6 portfolio, CXL smart solutions, and COSMOS software platform are embedded in the rack-scale infrastructure being built by hyperscalers, which are deploying the largest inference clusters in history.
Q1 2026 results came in above the consensus estimates and confirmed the momentum. Revenue of $308.4 million grew 93% year over year and 14% sequentially, reaching a new all-time quarterly record. The company launched and began initial shipments of its Scorpio X-Series 320-lane AI scale-up fabric switch, a product specifically designed for the high-radix inference topologies that hyperscalers are now deploying at scale. New design engagements for custom and optical solutions are further expanding the addressable market, and the merchant scale-up market that Scorpio targets is projected to reach $20 billion by 2030.
19 analysts cover the stock with a consensus Moderate Buy rating and a price target of $229.65, representing more than 15% upside. For investors seeking pure-play inference exposure in a company still early in a multi-year growth cycle, Astera Labs might be one of the most compelling names in the entire sector.
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