NVIDIA’s Rules Were Made To Be Broken

“Everything starts with CUDA.” — Jensen Huang, NVIDIA GTC Keynote

At a recent GTC keynote, NVIDIA’s CEO laid down the law: the future of accelerated computing is forged in CUDA, their domain-specific language, and anchored by a sprawling, vendor-locked ecosystem. To their credit, NVIDIA has built an impressive ecosystem around this technology – a testament to their innovation and market foresight. In fact, the CUDA ecosystem has become so valuable that we at NextSilicon designed our Intelligent Compute Architecture to support it fully.

However, Jensen’s underlying doctrine remains unambiguous: amass a massive install base of CUDA-compliant GPU systems, drive extensive library development, and create a self-perpetuating cycle where applications, hardware sales, and ecosystem dependencies continuously reinforce each other. This strategy has delivered tremendous value for specific workloads, but it shouldn't limit how we think about the future of accelerated computing.

“The larger the install base, the more developers want to create libraries. The more libraries, the more amazing things are done, leading to better applications and more benefits to users. They buy more computers, and the more computers, the more CUDA. That feedback path is vitally important.” This focused strategy has undeniably carved out monumental victories in graphics, AI, and scientific computing. But these aren't commandments etched in stone; they're rules. And these rules come with significant constraints:

The CUDA Cul-de-Sac: Porting your life’s work (your codebase) to CUDA isn't a weekend project. It’s a months-long, sometimes years-long, commitment that binds you to NVIDIA's roadmap, their pricing, and their whims.

The Portability Chasm: Let's be honest, not every application is a perfect candidate for GPU offloading. You can rewrite, debug, and optimize until you’re blue in the face, and still fall short of the promised land of speedups.

The Never-Ending Library Arms Race: Each new domain conquered demands its own bespoke, optimized framework. This translates to staggering R&D investments and a relentless, costly maintenance treadmill.

These constraints aren't just inconveniences—they're the direct result of a fundamental division that NVIDIA has not only accepted but actively reinforced and exploited. To understand why, we need to examine the artificial chasm they've created between computing paradigms.

The Great Divide: Accelerated vs. General-Purpose Computing

Jensen himself acknowledges a fundamental schism, which he shared during his recent Computex keynote: “However, accelerated computing is not general-purpose computing. In general-purpose computing, everybody writes software in, you know, Python or C or C++, and you compile it. The methodology for general-purpose computing is consistent throughout: write the application, compile the application, run it on a CPU. However, that fundamentally doesn’t work in accelerated computing because if you could do that, it would be called a CPU.”

He's right, in a way—but only if we accept that the limitations of current hardware architectures are immutable laws instead of temporary constraints. The current paradigm forces this artificial distinction because it serves NVIDIA’s business model, not because it’s an inevitable technical reality. When we pull back the curtain, this supposedly unbridgeable divide is merely a convenient narrative that reinforces the status quo.

Instead, imagine a reality where you write your application—any application—in the language you love, compile it once, and it just runs at exceptional speeds. No arcane kernel rewrites, no proprietary frameworks to master, no vendor allegiance tests. This is the holy grail of general-purpose computing, building upon decades of software engineering best practices and the "trillions of dollars of innovation" that Jensen himself acknowledges.

Breaking the Silicon Ceiling: Why Old "Rules" Demand New Architecture

This vision of universal acceleration challenges NVIDIA's fundamental narrative. Jensen dismisses the possibility of such seamless performance gains when he asks: “How is it possible that, all of a sudden, a few widgets inside a chip make computers 50× or 100× faster? That makes no sense.”

Jensen's skepticism reveals a critical assumption: that dramatic performance improvements must require equally dramatic changes to programming models. But what if this assumption is precisely what needs to be challenged?

He’s pointing out that incremental changes to traditional architectures have hit a wall. A von Neumann core, regardless of how many you creatively cram onto a die, won't magically deliver orders-of-magnitude leaps in performance without a fundamental paradigm shift. Even the most sophisticated many-core GPU still treats every instruction with a certain democratic inefficiency and demands domain-specific fealty to unlock its true power. This is where the old rules begin to crumble.

The Performance Paradox: How Jensen Revealed the GPU’s Limits

The key to breaking these rules lies in a surprisingly simple observation, one Jensen himself touched upon: “You can accelerate applications if you were to create an architecture that is better suited to accelerate—run at the speed of light—99% of the runtime, even though it’s only 5% of the code—which is quite surprising. In most applications, small parts of the code consume most of the runtime.”

This vision aligns exactly with what we discovered seven years ago. We were captivated by this very phenomenon: a tiny fraction of an application, often just 1-5% of the code, dictates 90-99% of its execution time. This extreme version of the Pareto principle wasn't just an observation; it became the cornerstone of our Intelligent Compute Architecture (ICA) and the Maverick chip that it powers. Unlike traditional GPU architectures constrained by a brute-force approach–accelerating all code equally–ICA takes a fundamentally different path. Our architecture intelligently identifies these critical hotspots and accelerates them to deliver efficient performance at scale.

NextSilicon: We're Not Bending the Rules, We're Rewriting Them

Here's what our revolutionary approach delivers—a complete redefinition of what's possible when you break free from NVIDIA's artificial constraints: NextSilicon’s vision isn’t an iteration; it’s a revolution. We’re not playing by the old rules; we’re architecting a new game where compute infrastructure:

Runs Everything, Without Compromise: Your existing CPU code, complex GPU kernels, demanding HPC tasks, and cutting-edge AI/ML models—run them all without code modifications.

Delivers Ludicrous Speed: Experience up to 10x speedups at a quarter of the power consumption. How? By dynamically optimizing silicon around your application’s hottest and resource-intensive code paths, in real-time.

Eliminates Vendor Lock-In: Forget proprietary Domain Specific Languages (DSLs). Forget tedious porting processes. Forget framework maintenance nightmares. Your code, your language, accelerated.

Future-Proofs Your Innovation: As your workloads evolve, ICA adapts. You’ll never slam into a "rewrite wall" again.

Here’s the Maverick magic: A sophisticated software profiler acts like a precision targeting system, continuously monitoring your application. It pinpoints that critical sliver of code hogging performance and then—at nanosecond granularity—reconfigures the hardware itself to forge custom dataflow pipelines optimized for that specific code. This asymmetric execution model directs exceptional efficiency precisely where it delivers maximum impact, leaving the bulk of your code to run as it always has.

Reconfiguring silicon on-the-fly at nanosecond speeds? It’s not just hard; it’s been deemed nearly impossible. It’s breathtakingly complex, utterly groundbreaking, and frankly, it’s why no one else has accomplished it. At NextSilicon, we didn’t just embrace this audacious challenge; we mastered it. Just as GPUs once boldly redefined the role of CPUs, NextSilicon's ICA-powered Maverick chip is here to rewrite the rules of computation. Jensen claimed "Everything starts with CUDA," but we're proving that everything actually starts with breaking free from arbitrary limitations. Today, Maverick delivers on the long-held dream: "write once, accelerate everywhere." No asterisks. No fine print. The rules were made to be broken, and the future of computing is now unbound.

Stay Connected

Stay tuned as we share more details in future blogs and announcements via our Nextletter.

About the Author:

Elad Raz is the founder and CEO of NextSilicon, a company pioneering a radically new approach to HPC architecture that drives the industry forward by solving its biggest, most fundamental problems.