AI Inference Chip War: Redefining the Semiconductor Landscape
The New Battlefield: AI's Shift from Training to Inference
In the spring of 2026, the artificial intelligence industry was rocked by a significant development: Reuters reported that Anthropic was exploring the possibility of designing its own chips. This AI laboratory, with annual revenue exceeding $30 billion and rapidly growing user base for its Claude models, was seriously considering transitioning from being a consumer of computing power to a definer of it.
Industry sources admitted that the related plans are still in early stages, with the company yet to finalize specific proposals or establish dedicated teams. Anthropic may ultimately opt to only purchase chips rather than design them independently. However, even the mere possibility of this move reveals several critical insights about the evolving AI hardware landscape.
Currently, Anthropic utilizes both Google's TPU (Tensor Processing Unit) and Amazon's Trainium chips for developing and running Claude. Just this week, the company signed a long-term agreement with Google and Broadcom, the latter being the core design partner for Google's TPU. This dual approach—securing billion-dollar external procurement agreements while quietly exploring in-house development—mirrors the strategy of Meta and Microsoft several years ago, both of which now possess their own proprietary chips.
The estimated cost to design a top-tier AI chip is approximately $500 million, but beyond the price tag, what's more noteworthy is the industry signal this move sends. When a pure model company begins seriously considering silicon design, the hardware race for AI inference has effectively entered a new phase of intensity.
The Great Migration: From Training to Inference
Over the past two years, the AI industry has undergone a profound transformation, with massive computing demand rapidly shifting from the training phase to the inference phase.
During training, processes can take weeks or even months, requiring large-scale GPU clusters for parallel computation. NVIDIA has established an almost unshakable dominant position in this domain. However, inference is fundamentally different. Inference involves the real-time computation that occurs every time a model responds to a user request, prioritizing low latency, high throughput, and low energy consumption—objectives that don't perfectly align with GPUs' strengths.
According to Barclays' predictions, by 2026, inference computing demand will account for over 70% of total AI computing needs—4.5 times that of training requirements. In essence, the true battleground for the future AI chip market lies in inference.
NVIDIA has built a decade-long moat around training, but if this defensive barrier cannot extend to inference, the entire industry landscape faces potential rewriting. This is precisely why NVIDIA took formal action at the end of 2025, announcing a non-exclusive licensing agreement with AI inference chip startup Groq. Subsequently, Groq's founder and CEO Jonathan Ross, president Sunny Madra, and several core engineers joined NVIDIA. Citing informed sources, the deal's consideration was approximately $20 billion.
NVIDIA's official wording was cautious, emphasizing only technology licensing and talent acquisition rather than a traditional acquisition. However, this atypical acquisition approach has become quite common in Silicon Valley, allowing companies to circumvent lengthy antitrust reviews while substantively incorporating target technologies and core teams.
Groq: The Rise and Fall of an Inference Challenger
Groq's story was initially compelling. Founder Ross was a core member of Google's TPU project and deeply understood the inherent limitations of GPU architecture in inference scenarios: thousands of parallel computing units and extremely complex memory scheduling logic—characteristics that are advantages during training but actually cause unpredictable latency jitter during inference.
Consequently, Groq chose a radically different path: completely eliminating hardware-level schedulers and instead having the compiler determine the data flow path at the code stage, allowing the chip to operate like an automated pipeline precise to the nanosecond. This architecture was named LPU (Language Processing Unit), and in mainstream large model inference testing, its word generation speed exceeded that of NVIDIA GPUs by more than ten times, while energy consumption per token was only one-tenth.
With this extreme performance, Groq attracted over 1.5 million developer users and secured multiple rounds of investment from top institutions including Cisco, Samsung, and BlackRock, with its valuation reaching $6.9 billion at one point. However, what made Groq could also break it. Its dazzling inference performance precisely made it the most critical target in Jensen Huang's sights.
On the surface, NVIDIA's acquisition of Groq appears to be completing the technical landscape for inference, but a deeper analysis reveals this as a defensive integration. By bringing one of the strongest external challengers into its ecosystem, NVIDIA has removed the bargaining chips from second-tier cloud providers and AI software companies without in-house chip capabilities. Without Groq as an alternative, options for companies unwilling to be "taxed" by NVIDIA have suddenly narrowed.
Cloud Giants' Response: The Rise of Inference ASICs
However, this predicament may not last indefinitely.
In fact, long before Groq's rise, major cloud giants had already been independently planning their computing power solutions. Google has TPUs, Amazon has Trainium, and Microsoft has Maia—all three in-house development routes have now reached maturity where they can be externally sold.
Google's seventh-generation TPU, codenamed Ironwood, is scheduled for official release and market launch in late 2025. Compared to its predecessor, its single-chip performance has improved by more than 4 times, with a single cluster capable of interconnecting up to 9,216 chips. Google's positioning for this generation is unambiguous: "the most cost-effective commercial engine for the inference era." From being forced to self-develop in 2015 due to internal computing bottlenecks to deploying TPUs in customer-owned data centers in 2025, Google has spent a decade transforming an emergency project into a strategic weapon. Anthropic's announcement that future Claude series training and deployment will use up to one million TPUs has further provided authoritative market validation for Ironwood's commercial value.
Amazon has taken a different approach. AWS has long relied heavily on chips self-developed by its Annapurna Labs. The Trainium series roughly competes with NVIDIA GPUs but focuses on reducing cloud infrastructure costs and dependence on external suppliers. AWS's recent multi-year cooperation agreement with Cerebras, planning to introduce Cerebras' wafer-scale engine (WSE) chips into data centers for parallel deployment with its self-developed Trainium chips, embodies this "self-development first, external procurement supplementary" logic.
AWS's target is clear: use Trainium to handle low-speed, low-cost inference needs, while using Cerebras chips to lock in high-end customers extremely sensitive to latency and willing to pay premiums for speed.
For inference chips, unlike training chips that prioritize short-term speed, long-term energy efficiency is more critical. An NVIDIA GPU consumes about 700 watts, while an equivalent computing power specialized inference chip can control power consumption to under 200 watts. For ultra-large-scale applications requiring hundreds of thousands of inference chips, this difference can save hundreds of millions of dollars annually. This is also one of the core reasons why cloud giants like Google, Amazon, and Meta are rushing to bet on ASIC (Application-Specific Integrated Circuit) chips.
The latest disclosures reveal that Meta and Broadcom have reached a $1Gw cooperation agreement for training and inference chips, which will undoubtedly bring new catalysts to an already "chaotic" inference chip market.
Intel and SambaNova: A Pragmatic Path Forward
If the cloud giants' in-house development route represents a long-term bet with sufficient resource backing, then Intel's collaboration with SambaNova represents another more realistic breakthrough path.
In 2026, SambaNova announced with Intel a heterogeneous hardware inference solution adopting a three-layer architecture: GPU responsible for pre-filling, Intel Xeon 6 processors as the main control and execution CPU, and SambaNova RDU responsible for decoding—specifically designed for intelligent AI workloads. This solution will be open to enterprises, cloud service providers, and sovereign AI projects in the second half of 2026.
SambaNova points out that pure GPU systems excel at parallel pre-filling stages, but in production environment inference tasks, CPU tool scheduling and specialized inference accelerator decoding efficiency are the key variables determining overall speed and cost.
Their test data shows that the LLVM compilation speed of Intel Xeon 6 processors exceeds 50% compared to server CPUs based on Arm architecture, with vector database performance up to 70% faster—two metrics that precisely hit the core performance bottlenecks of code intelligent agent workflows.
Intel's role in this cooperation is intriguing. The former PC powerhouse was almost marginalized from the main AI chip battlefield during the GPU era. Now, leveraging the CPU control and scheduling advantages of Xeon 6, it is regaining its presence in heterogeneous inference solutions. The data center software ecosystem being based on x86 architecture has also brought Intel back to the center of the AI stage.
Cerebras: From Startup to Cloud Supplier
Cerebras is another name worth detailing separately.
This startup focused on wafer-scale AI chips had submitted an IPO application in 2024, which was subsequently withdrawn, causing the capital market to doubt its prospects. However, shortly after, OpenAI signed a cooperation agreement with Cerebras worth over $10 billion to provide computing power for ChatGPT. This news brought Cerebras back into public view and caused previously观望 institutions to re-evaluate its technical value. In February 2026, Cerebras completed a new $1 billion financing round, with total financing reaching $2.6 billion and a post-investment valuation of approximately $23 billion.
Cerebras' core technology is the wafer-scale engine (WSE), using an entire wafer as a single chip, breaking through the physical cutting limitations of traditional chips and showing extremely excellent latency performance in specific inference tasks. According to Cerebras' claims, its chip's speed in the inference decoding stage can be up to 25 times that of NVIDIA GPUs.
AWS's announcement of a multi-year cooperation agreement with Cerebras, introducing WSE chips into data centers for AI inference, marks a critical identity leap for this startup—from a financing story to a supplier of the world's largest cloud platform.
AWS's choice of Cerebras is consistent with OpenAI's logic: for scenarios extremely sensitive to response speed like programming assistance and intelligent agent tasks, every millisecond of latency reduction directly impacts user experience and commercial value—and this precisely happens to be GPUs' Achilles' heel.
For Cerebras, as more people use AI to solve increasingly difficult problems, the demand for speed will only increase, not decrease. If speed itself is part of product value, then paying a premium for speed is理所当然 commercial behavior. This logic is being accepted by more and more enterprise clients.
CoreWeave: The Infrastructure Enabler
The flip side of the computing power battle is the reconstruction of the infrastructure supply side. In this domain, CoreWeave's role is becoming increasingly indispensable.
In 2025, Meta signed a supply agreement with CoreWeave, agreeing to purchase $14.2 billion in AI computing power by 2031; recent SEC filings show Meta has added an agreement to purchase an additional $21 billion in computing power by 2032. This new agreement has pushed CoreWeave's order backlog to $87.8 billion, with Meta accounting for about 40% of this.
CoreWeave's rise is a microcosm of the process where GPU computing power is evolving from a scarce commodity to infrastructure. As a pure computing power lessor, it doesn't provide model capabilities but rather the underlying support that enables models to run. Beyond the three major cloud giants, AI companies need a computing power option not bound to platform ecosystems, and CoreWeave恰好 fills this gap.
In 2025, CoreWeave achieved sales of $5.13 billion, a year-over-year increase of about 1.7 times. Its data center scale has expanded to 43 facilities, with a power capacity of 850 megawatts. The company is equipped with approximately 600,000 GPUs, mainly NVIDIA H100 and H200, with the Blackwell series proportion continuously increasing. Its total contracted power capacity has reached 3,500 megawatts—more than four times its current usage capacity.
However, CoreWeave's expansion logic is also its biggest structural pressure. To cover data center expansion costs, the company recently announced a private placement of bonds totaling $4.75 billion. With cash on hand of less than $4 billion, completing $30-35 billion in capital expenditure in 2026 means relying on external financing to maintain high-speed expansion. CoreWeave's investors are clearly betting on the core judgment that computing power demand will continue to grow strongly in the long term.
The Future Landscape: Heterogeneous Coexistence
Anthropic's exploration of self-developed chips, NVIDIA's $20 billion acquisition of Groq, Google's decade-long effort turning TPU into a benchmark product, Amazon introducing Cerebras into its data centers to build a differentiated inference portfolio, Intel joining forces with SambaNova to compete in the heterogeneous inference market—these seemingly scattered events all point to inference as the new battlefield.
More and more people are realizing that AI's focus is shifting from how to train better models to how to infer more requests at lower cost and faster speed. This transformation is causing the previously GPU-centric computing system to undergo a massive change.
This round of competition differs from the early GPU replacement of CPUs. That was a one-way crushing of a new product over an old one. Today's inference chip battle is more like a division and reorganization within a complex ecosystem. No single architecture can dominate all scenarios, and heterogeneous combinations are becoming mainstream. GPUs handle highly parallel pre-filling, specialized inference chips承担解码, CPUs handle scheduling and coordination, with cloud and edge endpoints having different focuses—multiple players compete at each link.
This also means the outcome is far from decided.
For Anthropic, exploring self-developed chips is an active pursuit of computing power autonomy and an insurance policy against being held hostage by upstream suppliers. However, the long cycle and high investment of chip development mean this path won't be easy. For NVIDIA, the CUDA ecosystem moat remains deep, but the increasingly obvious performance-cost gap in the inference end is becoming a breakthrough point for all potential challengers. For other technology competitors like Groq, technological leadership doesn't necessarily translate into commercial victory, and the possibility of acquisition continues to grow.
The battle lines have been drawn, and the list of participants continues to expand. This AI inference computing power melee has only just entered its most intense chapter.
