What is NCU? A New Way to Understand AI Compute Power

Imagine walking into a supermarket where every product is sold by a different measurement: apples by the pound, bananas by the dozen, strawberries by the box. You’d need to constantly convert and compare—"Wait, how many strawberries are in a box? Is this banana deal actually good compared to the apples?"

Now imagine doing that with something far more abstract: AI computing power.

That’s the reality today for AI developers, researchers, and businesses trying to rent or sell GPU time. Platforms like AWS, Vast.AI, Lambda, and CoreWeave all offer GPU compute in different ways: by the hour, by the GPU type, by instance, by performance score. Each platform speaks its own language. Comparing across them is like translating five dialects at once—while trying to plan your next AI workload.

At NiceGPU, we believe there’s a better way. That’s where NCU (NiceGPU Compute Unit) comes in.

What Is NCU?

In the simplest terms, 1 NCU = the compute power of an NVIDIA A100 80GB running for 24 hours. That’s it. No confusion. No guesswork.

We chose the A100 because it’s a modern workhorse in AI training—fast, memory-rich, and widely recognized.

This unit becomes your baseline. Every other GPU, whether it’s a V100, H100, RTX 3090, or even a shiny new H200, can be expressed in fractions or multiples of NCU based on real-world performance.

So instead of asking, “How many GPU hours do I need?” or “Is this $0.89/hour RTX 3090 better than$ 2.49/hour A100?”, you can just ask: “How many NCUs does my job need?”

The Problem NCU Solves

The AI compute world today is fragmented:

Pricing is inconsistent. One provider charges by GPU type, another by instance, another by performance score.
Performance isn’t always transparent. A 24GB GPU may sound good—until it runs out of memory halfway through training.
Comparing costs is a headache. You might rent an RTX 4090 for two days and wonder if that was smarter than renting a single A100 for one.

NCU fixes this by creating a common yardstick. It’s like introducing kilowatt-hours to an industry that used to sell electricity by the spark.

How NCU Changes the Game

With NCU:

Users can plan and price workloads consistently.
- “This fine-tuning job needs ~1.2 NCUs. I can split that across 2 RTX 3090s over 24 hours or use a single A100 for one day.”
Suppliers can list resources in standardized terms.
- “My setup provides 0.6 NCU/day. Let’s price accordingly.”
Marketplaces can exchange compute power like a token.
- Instead of "renting an RTX 3080 for $0.45/hr," buyers can purchase 0.5 NCU and use it however they like.

It becomes easier to make decisions. Easier to optimize. Easier to trust what you're buying or selling.

But Wait—Are There Challenges?

Absolutely. NCU isn’t a silver bullet (yet). Here are a few things we’re mindful of:

Different tasks scale differently across GPUs. Some models favor memory, others favor raw TFLOPs.
Memory matters. A GPU with only 16GB may offer decent compute but still fail larger models that require 80GB.
Trust in benchmarks. To map different GPUs to NCUs, we rely on standardized performance benchmarks.

NiceGPU provides additional details like memory and bandwidth when listing GPUs, and the community can help refine the NCU model over time.

What’s Next for NCU?

We envision a future where NCUs are more than just a measure—they’re a token.

You could hold NCUs like cloud credits, exchange them, redeem them on different platforms, or even optimize workload scheduling by NCU efficiency.

It’s a future where GPU compute is understandable, exchangeable, and democratized.

At NiceGPU, we’re building that future.

So next time someone asks how much GPU power you need for your next AI model, tell them: “About 0.75 NCUs should do.”

Simple, right?