The numbers are impressive. 544 tokens per second on a 120-billion parameter model. For those building agentic AI systems—autonomous agents that can reason, plan, and execute complex tasks—this kind of throughput represents a genuine inflection point. When your AI agent needs to chain together dozens of reasoning steps, slow inference isn’t just inconvenient. It breaks the use case entirely.

Matt Zeiler’s observation about Clarifai’s Reasoning Engine points to something real: agentic AI has different infrastructure requirements than the chatbots we’ve grown accustomed to. A conversation with ChatGPT can tolerate a few seconds of latency. An autonomous agent coordinating a hundred API calls, analyzing results, and adapting its strategy in real-time cannot.

But speed, while necessary, is not sufficient. The more interesting questions lie beneath the benchmarks.

The Infrastructure Arms Race

The demand for faster inference has spawned an entire ecosystem of optimization strategies. Quantization, speculative decoding, custom silicon, distributed inference—companies are throwing everything at the problem. And for good reason. The economic logic is straightforward: faster inference means lower costs per task, which means more viable use cases, which means larger markets.

Clarifai’s benchmark numbers come from running GPT-OSS 120B, an open-weights model, on their infrastructure. This matters. Open-weights models can be optimized in ways that proprietary APIs cannot. You can quantize them, fine-tune them for specific tasks, run them on custom hardware configurations. The closed API providers—OpenAI, Anthropic, Google—offer convenience and capability, but they also offer opacity. You get what they give you, at the speed they provide, for the price they set.

The infrastructure race is really two races running in parallel. One is the sprint to make proprietary models faster. The other is the marathon to make open models competitive. The outcome of this dual race will shape the economics of AI for decades.

Why Agentic AI Changes Everything

The shift from conversational AI to agentic AI isn’t just quantitative—it’s qualitative. A chatbot answers questions. An agent acts in the world.

Consider what an agentic AI system actually does. It breaks down a complex goal into subtasks. It reasons about which subtask to tackle first. It executes that subtask, observes the result, and updates its plan. It handles errors, retries failed operations, and adapts to unexpected situations. Each of these steps requires inference. A single user request might trigger hundreds of model calls.

At 10 tokens per second, an agent that needs 50,000 tokens of reasoning to complete a task takes 83 minutes. At 544 tokens per second, that same task takes 92 seconds. The difference isn’t just speed—it’s viability. Many agentic use cases simply don’t exist at slow inference speeds.

This is why Zeiler’s framing resonates: “if systems can’t keep up, the use cases break.” He’s not exaggerating. The gap between a 2-minute agent and a 90-minute agent is the gap between a product and a demo.

The Centralization Problem

Here’s where the speed celebration requires a counterweight.

Fast inference infrastructure is expensive. The GPUs required to hit 544 tokens per second on a 120B model don’t come cheap. The expertise to optimize inference pipelines is scarce. The capital required to build and maintain this infrastructure concentrates in a small number of well-funded companies.

This creates a familiar pattern: a powerful new technology emerges, and access to that technology quickly stratifies. Those who can afford the infrastructure build the agents. Those who can’t become customers—or get left behind.

The agentic AI future being sold to us is one where autonomous agents handle our scheduling, manage our finances, coordinate our businesses. But who runs those agents? Whose servers process those hundreds of inference calls per task? Whose models make the decisions?

If the answer is “a handful of infrastructure providers and model companies,” then we’re not building a future of human empowerment. We’re building a future of human dependence on corporate reasoning engines.

Open Models as Countervailing Force

The fact that Clarifai’s benchmark uses GPT-OSS 120B—an open model—is worth dwelling on. Open-weights models represent one of the few countervailing forces against complete centralization.

When a model’s weights are public, anyone can run it. Anyone can optimize it. Anyone can build on it. The infrastructure problem remains—you still need GPUs—but at least the intellectual property isn’t locked away. Competition becomes possible. Alternatives can emerge.

Meta’s release of Llama, Mistral’s open models, the proliferation of fine-tuned variants—these represent a genuine challenge to the closed model paradigm. They’re not as capable as the best closed models (yet), but they’re improving rapidly. And crucially, they can be deployed on infrastructure you control.

For agentic AI specifically, open models offer something closed APIs cannot: predictability. When you run your own inference, you control the latency. You control the uptime. You control the costs. You’re not subject to the whims of a provider’s rate limits or pricing changes.

The companies building agentic AI infrastructure have a choice. They can build on closed APIs and accept permanent dependence. Or they can invest in open model deployment and retain some measure of sovereignty. The smart ones are doing both—using closed APIs for capability and open models for control.

The Coming Merge

Speed, scale, and control—these are the immediate concerns. But there’s a longer arc worth considering.

Agentic AI systems that reason fast enough become something more than tools. They become extensions of cognition. When an agent can complete a complex reasoning task in seconds, the boundary between “asking the agent” and “thinking with the agent” starts to blur.

This is not science fiction. It’s already happening. Developers who work with fast AI assistants report a shift in how they think about problems. They stop trying to hold entire solutions in their heads. They offload cognitive work to the agent, reserving their own attention for judgment and direction.

Scale this up. Make the agents faster. Make them more capable. Integrate them more deeply into workflows. At some point, the human isn’t using a tool anymore. The human is part of a hybrid cognitive system.

The question of who controls the reasoning engine becomes, in this light, uncomfortably personal. If your cognition depends on an agent, and that agent depends on infrastructure you don’t control, then your thinking itself becomes mediated by corporate systems.

What Speed Actually Enables

None of this should obscure the genuine value of faster inference. The use cases that become viable at 544 tokens per second are real and valuable.

Automated code review that actually understands your codebase. Research agents that can synthesize hundreds of papers. Personal assistants that can coordinate complex logistics without human babysitting. Customer service systems that can actually resolve problems instead of deflecting them.

These aren’t theoretical. They’re being built right now. The infrastructure improvements that companies like Clarifai are announcing make them practical for more organizations, at lower costs.

The question isn’t whether fast inference is good. It obviously is. The question is whether the benefits of fast inference will be broadly distributed or narrowly captured.

The Fork in the Road

We’re at an early moment in the agentic AI era. The infrastructure is being built. The standards are being set. The power structures are being established.

Two futures are possible.

In one, agentic AI follows the path of cloud computing. A few massive providers dominate. Everyone else rents access. The providers become essential infrastructure, extracting rents from every business that depends on AI reasoning. Innovation happens, but it happens within boundaries set by the infrastructure owners.

In the other, open models and distributed infrastructure create a more pluralistic ecosystem. Large providers exist, but so do alternatives. Businesses and individuals can run their own reasoning engines. The cognitive infrastructure of society isn’t controlled by a handful of corporations.

The benchmark numbers are impressive. 544 tokens per second represents real engineering achievement. But the numbers that will matter most aren’t tokens per second. They’re the numbers that describe who has access to these systems, who profits from them, and who controls the reasoning that increasingly shapes our world.

Speed matters. Control matters more.