Nvidia, Groq and the Real-Time AI Pyramid: Why Latency Is the Next Enterprise Bottleneck

From a distance, AI progress looks like a smooth exponential curve. Up close, it is a staircase: long plateaus punctuated by abrupt step changes when a new architecture or paradigm breaks through a bottleneck.

For enterprise leaders planning multi-year AI investments, understanding where we are on that staircase is no longer academic. The shift from training-centric AI to real-time, reasoning-heavy AI agents is exposing a new bottleneck: latency at inference time. That is where Nvidia’s GPU dominance, Groq’s Language Processing Units (LPUs), and emerging techniques like Mixture-of-Experts (MoE) intersect—and where enterprises can either unlock new capabilities or hit a wall of user frustration and runaway cost.

The staircase of AI growth: from CPUs to GPUs to LPUs

For decades, Moore’s Law created the illusion of smooth, predictable compute growth. CPU performance doubled regularly—until it didn’t. CPU gains flattened, and the next “limestone block” in the pyramid of compute turned out to be GPUs.

Nvidia’s long bet on GPUs, starting with gaming and then computer vision, became the foundation for today’s generative AI wave. The company effectively turned GPUs into the universal engine for both training and inference. That shift was itself a step change: when transformer architectures emerged, GPUs unlocked the ability to train deeper, more capable models at scale.

But AI progress has never been a single, continuous curve. It is a sequence of bottlenecks being broken:

• We could not calculate fast enough — GPUs solved the parallel compute problem.
• We could not train deep enough — transformers unlocked large-scale language and vision models.
• Now, we cannot “think” fast enough in real time — and that is where LPUs enter the picture.

Recent developments underscore that we are again mid-shift. For example, DeepSeek demonstrated in late 2024 that a world-class model could be trained on a comparatively small budget, in part by using MoE techniques. The point was not just cost efficiency; it highlighted that future gains will come from smarter architectures, not simply more brute-force GPU scaling.

Nvidia’s own Rubin platform messaging reflects this. The company is promoting interconnect and MoE capabilities to deliver “massive-scale MoE model inference at up to 10x lower cost per token.” The direction of travel is clear: efficiency, specialization and new architectures are becoming as important as raw FLOPs.

Why latency, not just FLOPs, is now the strategic constraint

The past few years of AI competition have focused on model size, benchmark scores and training runs. But in 2025, some of the biggest advances in practical AI capability have come from a more mundane-sounding dimension: inference-time compute—letting models “think” longer before answering.

For enterprise use cases, this is critical. Autonomous or semi-autonomous AI agents tasked with booking travel, drafting contracts, researching legal precedent or generating complex code often need to run long internal reasoning chains. Instead of a model generating a few hundred tokens in a straight line, it may need to spin through thousands of intermediate “thought tokens” to verify, self-correct and explore alternative paths before returning a final answer.

That deeper reasoning can dramatically improve reliability and usefulness—but it also magnifies latency. Time is money in two ways: inference-time compute costs grow with every token, and human users (or downstream systems) will not wait indefinitely for results.

This is the emerging “latency crisis” for real-time AI:

• Enterprises want agents that are more autonomous and trustworthy, which implies more internal computation.
• Each increment of internal reasoning time increases the risk of user abandonment and higher infrastructure spend.

Traditional GPU-centric stacks, optimized for massive parallelism and large training batches, are not inherently tuned for this pattern of small-batch, reasoning-heavy inference where end-to-end response time is the metric that matters.

Groq’s LPU: a specialized answer to the “thinking time” problem

Groq’s approach is to attack this latency bottleneck with a purpose-built architecture: the Language Processing Unit. Where GPUs excel at parallel throughput, LPUs are designed to minimize the memory bandwidth bottlenecks that arise during small-batch inference, especially for reasoning workloads.

In practical terms, that architectural choice translates into dramatically faster token generation for many inference scenarios. The implications for long-chain reasoning are stark. Consider a model that needs to generate 10,000 internal thought tokens before producing a single visible output:

• On a standard GPU setup, that process might take 20–40 seconds—long enough for a human to lose patience or for a real-time workflow to break.
• On Groq hardware, the same internal reasoning can reportedly be executed in under 2 seconds.

For an enterprise deploying AI copilots or agents, that difference is not incremental—it determines whether a new class of applications is viable at all. The ability to “out-think” competitors’ models at similar or lower perceived latency effectively turns raw inference speed into user-perceived intelligence.

Groq alone is not the full story. The real leverage appears when you combine:

• Architecturally efficient models (such as those using MoE, as in DeepSeek’s work), and
• High-throughput, low-latency inference hardware (such as LPUs).

Together, they offer “frontier-class” behavior at lower cost and with near-instantaneous interaction. For enterprises, that combination is the difference between a demo-ready prototype and a production system that can be scaled across customers, employees or products.

Nvidia’s position: from universal GPU hammer to dual-engine platform

For roughly a decade, the Nvidia GPU has been the universal hammer for AI: H100s to train, and the same or slightly trimmed variants to serve inference. That simplicity created standardization—and a substantial moat for Nvidia’s ecosystem.

But as models adopt more “System 2” style thinking—reasoning, self-correction and iterative planning—the nature of inference diverges further from training. Training remains a brute-force, massively parallel exercise. Reasoning-heavy inference, by contrast, is latency-sensitive and sequential in feel, even when implemented with parallel primitives.

This raises the prospect of a dual-engine world:

• GPUs as the primary engine for training and for high-throughput, batch-style inference.
• LPUs (or LPU-like architectures) as the specialized engine for real-time reasoning and agentic workloads.

If Nvidia were to integrate a Groq-like architecture into its portfolio, it could extend its dominance from “rendering intelligence” (training and basic inference) to “rendering reasoning” in real time. Just as it moved from gaming pixels to generative content, the company could move again, this time toward making complex, multi-step thought processes feel instantaneous to end users.

There is also a software dimension. Groq’s challenge has been building a robust software ecosystem around its hardware. Nvidia, on the other hand, has CUDA and a deeply entrenched developer base. Wrapping that mature ecosystem around a low-latency inference architecture would create a powerful moat: a single environment where enterprises can both train cutting-edge models and deploy them with minimal latency.

Pair that with a next-generation open model—such as a successor to DeepSeek’s work—and you have a credible path to offerings that rival today’s proprietary frontier models on cost, performance and speed, especially for inference-heavy, real-time scenarios.

Strategic implications for the C-suite: designing for real-time reasoning

For enterprise technology leaders, the key question is no longer whether to adopt AI, but how to architect for the next step on the pyramid—where reasoning speed, not model size alone, differentiates winners.

Several implications emerge from the current trajectory:

1. Treat latency as a first-class business requirement, not just a technical metric. If your agents need to run thousands of internal reasoning steps, end-to-end response times in the tens of seconds are likely unacceptable. Requirements for customer-facing apps, internal copilots and autonomous workflows should explicitly capture tolerable “thinking time.”

2. Separate training strategy from inference strategy. The era of a single universal chip for all workloads is fading. Training may continue to consolidate on large GPU clusters, but inference architectures can and likely will diversify. Planning should reflect a heterogeneous future with different hardware for different stages of the AI lifecycle.

3. Align model architecture choices with hardware characteristics. Techniques like MoE enable substantial efficiency gains, but their benefits depend on how well the underlying hardware and interconnect are matched to sparse, expert-based routing and large-scale inference. Infrastructure and model decisions should be made together, not in isolation.

4. Evaluate provider moats and ecosystem lock-in consciously. If Nvidia were to pair GPU training with low-latency inference hardware and its existing software stack, it would deepen its platform moat. That may offer compelling performance and simplicity, but it also concentrates dependency. Enterprises should weigh the advantages of a unified stack against the strategic risk of over-consolidation.

5. Pilot long-chain, agentic workloads early. The difference between 2 seconds and 30 seconds of “thinking time” is qualitative, not just quantitative. By experimenting now with agentic use cases that push internal token counts high, you can better understand where current infrastructure breaks and where new architectures could unlock transformational value.

Climbing the next block in the AI pyramid

The story of AI infrastructure is a sequence of constraints being shattered. GPUs addressed the need for parallel compute; transformers addressed the need to train deep, expressive models. The emerging constraint is thinking fast enough—running complex reasoning chains without losing users or blowing up budgets.

In that context, Groq’s LPU architecture represents a potential third block in the pyramid: a specialized engine for real-time reasoning. Nvidia has a track record of embracing architectural shifts to stay atop each new step, even when it means reshaping its own product lines.

For enterprises, the takeaway is not to bet on a specific vendor, but to recognize the pattern. The apparent smooth curve of “AI progress” hides a jagged staircase of paradigm shifts. Those who design infrastructure, operating models and vendor strategies around the current block risk being surprised by the next one.

Planning now for a world where low-latency reasoning is a core requirement—alongside training scale and model quality—positions organizations to use, not fight, the next step in the AI pyramid.