Today, the AI landscape is shifting dramatically. Over the last 12 months, the demand to deploy trained models in real-time applications has surged. AI inference—the process of using a trained AI model to make predictions or decisions by crunching new AI data inside networked computers like servers or data centers—has become a critical and complex area of growth. This is underscored by deep investment and a projected compound annual growth rate (CAGR) of 19.2% by 2030. Complexity and cost are deeply intertwined, driven by model size, data processing, and required computational resources, all factoring into successful ROI-generating deployments.
At GTC25, Nvidia CEO Jensen Huang talked in-depth about the need to produce dramatically more tokens for less cost. This is the scale-up and scale-out challenge. But achieving the token-to-cost ratio required for mass AI inference adoption means moving beyond long outdated CPU (x86) and NIC (network interface controller) architectural approaches.
The economics are broken, and until that is fixed, the full value of AI inference won’t be realized. Here’s the stark reality: processing generative AI tokens is at least 10 times too expensive in AI servers. This inefficiency plagues every AI model—text, images, video, audio, or multimodal combinations.
It stems primarily from our reliance on the traditional x86 CPU—the de facto industry standard for decades. While the CPU was once the undisputed hero of PCs and the internet, powering complex software from Pixar movies to NASA supercomputers, it has become the silent enemy of scalable, efficient AI.
Today, it’s a race to commoditize generative and agentic AI tokens. Who can do it best, fastest, and most affordably? What disruptive breakthroughs are required, and who is ready now?
How Did We Get Here?
To answer, let’s rewind to the Deep Learning Revolution (2012-2015)—a seismic shift undeniably kicked off by AlexNet’s 2012 breakthrough. This landmark event saw a neural network shatter previous records in image recognition, proving deep learning could deliver unimaginable real-world performance.
Crucially, AlexNet’s success was powered by an unlikely hero: Nvidia’s GPUs. Designed for video games, their unique parallel processing architecture proved incredibly well-suited for the massive, repetitive calculations of neural networks. Nvidia had invested heavily in making GPUs programmable for general computation, an unforeseen golden ticket in the AI boom. This rapid adoption meant GPUs surged ahead in AI compute capabilities, a trend that Jensen Huang himself later formalized as Huang’s Law: the observation that GPU performance for AI will more than double every two years. This phenomenon allowed GPUs to leave traditional CPUs in their dust for AI’s specific demands.
You’ve got GPUs and other AI accelerators, but it’s like equipping a Formula 1 car with a powerful engine only to connect it with bicycle chains. That’s our reliance on x86 CPU architecture—the foundational design behind most current servers, dating back over 30 years—to drive modern AI.
Legacy CPUs: A Real Business Drag for AI
Why are traditional general-purpose CPUs such a critical business and performance drag for AI inference?
- The CPU is a bottleneck, plain and simple. Your powerful GPUs are AI factories, but the x86 CPU acts as a generalist traffic controller. It gets overwhelmed managing data, leaving expensive GPUs idle and delivering only a fraction of their horsepower.
- Network and data movement are massively inefficient. AI demands massive data flow. In traditional architecture, network data takes a costly detour through the x86 CPU, causing delays and consuming CPU power just moving data, not doing AI work. An AI-NIC could help so that AI data could completely bypass the CPU.
- CPUs are not built for AI’s future. The x86 architecture, designed for general enterprise computing, is a poor specialist for AI’s intense parallel computations. This mismatch wastes energy, limits scaling, and forces a square peg into a round hole as one of our partners observed.
The Rise of the AI-CPU
A new class of specialized, purpose-built inference chips is emerging, fundamentally rethinking computing and connectivity for AI. This isn’t another GPU or accelerator; it’s an innovation to the core CPU itself.
How does this inference chip – an AI-CPU – differ from the past?
- Built for AI inference: An AI-CPU is custom-designed for real-time AI, prioritizing speed and efficiency.
- Enables unified processing and AI networking: The AI-CPU tightly integrates processing with high-speed network access, eliminating data bottlenecks. Specialized chips that subsume x86 CPU and NIC function into one, providing purpose-built AI networking directly, and adds hardware orchestration, video/audio encoders/decoders, and AI-hypervisors for a hardened, economical solution.
- Delivers total system optimization: An AI-CPU redesigns the entire AI pathway in hardware, maximizing GPU and AI accelerator utilization from below 50% to nearly 100%. This cuts energy consumption and extracts far more value from expensive GPU investments.
The result: significantly more AI token output for the same cost and power, transforming AI adoption economics and business user experiences.
The Full Spectrum of AI Efficiency
While an expanded class of AI-CPUs marks a pivotal shift in hardware, the industry is also pushing boundaries in other critical areas to make AI inference even more efficient.
Consider software optimization: brilliant minds refine AI models through techniques like “pruning” (trimming unnecessary elements) and “knowledge distillation” (enabling smaller models, like DeepSeek, to perform remarkably well). This makes AI models smarter, lighter, and dramatically boosts inference speed through data flow optimization.
Hardware advancements in GPUs and other AI accelerators march relentlessly, far outpacing traditional CPUs. This rapid acceleration, dubbed Huang’s Law, observes that GPU performance for AI more than doubles every two years—driven by architectural breakthroughs, improved interconnects, advanced memory, and smarter algorithms, not just more transistors. Yet, paradoxically, their immense power is held back, like Ferraris stuck in traffic due to a bottleneck.
To truly unleash those powerful yet expensive AI accelerators and finally slash the cost per AI token, we need high-performance, hardware-driven AI orchestration—acting like the front-line traffic cop for fast incoming AI inquiries during peak periods of potential congestion. This also includes the urgent development of specialized AI NICs crucial for measuring and improving metrics like time to first token (TTFT) and bypassing today’s networking bottlenecks.
This intelligent choreography of every task and seamless system integration, embedded directly in silicon, is precisely the kind of purpose-built design an AI-CPU brings to the table. As these novel approaches gain traction, such smart software and revolutionary hardware innovations could drive the cost per AI token toward near-zero marginal cost for each additional one.
Think of the Uber ride-sharing service: once the core system is built, adding a car or ride request comes at virtually no extra expense, allowing massive, flat-cost scaling. Ultimately, this unified approach of smart software and groundbreaking hardware will commoditize AI token production, making it truly profitable for any government or business – the tech industry notwithstanding.
The Path to Profitable AI
Today, despite massive capital investments, AI inference operational costs remain stubbornly high. Big Tech, including cloud service providers, often face negative margins, pouring money into current AI inference systems without addressing fundamental architectural flaws. The sheer computational expense of memory operations, attention mechanisms, and matrix multiplication keeps token production costly.
For any business, profitability hinges on driving down the marginal cost—the expense of producing one more unit. A ride-sharing platform adds requests at near-zero cost once built. Similarly, a successful lender sees almost zero incremental financing cost for each additional dollar of funding. Without this low marginal cost, businesses aren’t profitable long-term.
This principle applies to AI. By driving down the true marginal cost of generative AI tokens, the market will stop subsidizing expensive operations. That’s why it’s so important to commoditize AI tokens – increasing the token per dollar or decreasing the cost per token – delivering real business value through unparalleled productivity and revenue.
To achieve this, we must forge a new path: superior inference computing, networking, orchestration, and full silicon utilization. This means a reimagined AI inference architecture powered by AI-CPUs that integrate AI-NIC capabilities within a single chip – and that work in perfect harmony with AI accelerators.
This integrated approach is how we close the innovation gap between Moore’s Law and Huang’s Law, paving the way to truly profitable AI and near-zero marginal cost for every additional AI token.
Isn’t it time to embrace a fresh approach that can lower the barriers to AI adoption and unlock unprecedented business value?
