What is TOPS and Why Should You Care?

Beware: Not all TOPS are created equal, and chasing the highest number can lead you straight into the wrong computing hardware for your actual workload.

If you’re a fan of AI chip specs (you’re cool, like us), there’s a good chance you’ve seen the acronym TOPS.

It could be a slick new edge module or a big-name accelerator built for data center AI training and inference. TOPS is a way to measure how many trillions of operations a chip can handle every second.

Short for “Tera Operations Per Second”, it sounds impressive, and it is… sort of.

It’s become the go-to number for benchmarking the raw processing capability of AI hardware, especially for inferencing the part of AI that takes a trained model and uses it to make decisions, spot patterns, or recognize objects in the wild.

Industrial systems don’t have time to send data to the cloud and wait for a response. Decisions need to happen on the spot, whether it’s a safety stop on a factory line or a thermal anomaly in a remote substation. That’s where local AI inference comes in. It keeps things fast, responsive, and reliable.

Chipmakers like NVIDIA, AMD, Intel, Qualcomm, and Hailo often highlight TOPS as a headline figure for their AI hardware. You’ll see specs like “up to 100 TOPS” or “50 TOPS at INT8” featured prominently in their product materials.

Types of operations: INT8, FP16, FP32

Here’s where things get a little tricky. When you see “100 TOPS” in a spec sheet, you’d think it’s a clear indicator of performance. But it depends entirely on what kind of operations are being measured.

AI processors handle math at different levels of precision. The most common ones are INT8, FP16, and FP32.

They reflect how numbers are represented and calculated under the hood. INT8 uses 8-bit integers. FP32 uses 32-bit floating-point values. The smaller the number size, the faster and more energy-efficient the chip can go.

So, when a chip claims “100 TOPS at INT8”, that doesn’t mean it performs anywhere near that at FP16 or FP32. You might only get 25 TOPS at FP16 and even less at FP32.

Now, why does that matter?

Most modern AI inference at the edge doesn’t need FP32 precision. INT8 offers a strong balance between accuracy, speed, and efficiency, especially for image classification, object detection, and other vision-based models. That’s why edge-focused accelerators like Hailo or NVIDIA Jetson prioritize INT8 performance.

The tradeoff is precision.

Lower-bit operations can lose a little accuracy, which might be a problem for certain models, such as high-stakes medical diagnostics or financial predictions. But for many edge workloads, the speed and efficiency gains of INT8 far outweigh the minor accuracy loss.

It’s like sending a text instead of a handwritten letter. The text gets there faster, easier, and more efficient, but it loses the personal touches and the finer details.

Architectures and how they influence TOPS

Two chips can claim the same TOPS rating and perform wildly differently in the real world. The reason comes down to architecture.

A CPU, GPU, and NPU might all run AI workloads, but they’re built for different strengths.

• CPUs are generalists. Flexible, great at sequential tasks, and able to handle a bit of everything. But they can’t match the raw parallelism of a GPU or dedicated AI chip for deep learning inference.
• GPUs are masters of parallel computation. They can run thousands of operations at once, making them ideal for large AI models. That said, they draw more power and often require active cooling, which isn’t always practical at the edge.
• NPUs, TPUs, ASICs, and FPGAs are the specialists. They’re designed from the ground up to accelerate specific types of AI math, matrix multiplications, convolutions, and so on. That laser focus is why something like a Hailo-15 module can run certain vision models faster than a much “bigger” GPU, despite having far fewer TOPS on paper.

Memory bandwidth plays a huge role. If your chip can crunch numbers faster than it can fetch data, you’re leaving performance on the table. Thermal limits matter, too. A fanless industrial edge server might run at full speed for a short burst but then throttle to stay within a safe temperature range.

That’s why a well-designed edge AI device with “lower” TOPS can outperform a general-purpose chip with a higher number.

It’s about balance, matching compute capability with memory, cooling, and workload fit.

Why some models or chips have higher TOPS

When a chip posts a massive TOPS number, it’s the result of deliberate design choices; things like smaller transistors, higher power budgets, and dedicated AI accelerators.

A lot comes down to process node size. Smaller nodes (measured in nanometers) mean transistors can be packed closer together, switch faster, and consume less power. That opens the door for more cores, bigger accelerators, and higher clock speeds, all of which push TOPS higher.

Power envelope is also important. A chip running inside a 300‑watt data center accelerator has far more thermal headroom than one sealed inside a passively cooled edge device. More power means more performance, but it also means bigger cooling solutions and higher energy costs.

Architectural decisions play a huge role. Wider buses allow more data to flow per cycle. On‑chip memory reduces time spent fetching data from slower external RAM. AI‑specific accelerators, matrix multipliers, tensor cores, vision DSPs, can crank through certain workloads at a fraction of the time a general-purpose core would take.

Some chips are built with specialized AI logic, optimized for inferencing and hit 50 TOPS by itself. By contrast, a standard desktop CPU with no dedicated AI acceleration might look anemic in TOPS even if it runs at higher base clock speeds.

Higher TOPS is the sum of design choices aimed at squeezing more math out of every watt, cycle, and square millimeter of silicon.

Does Higher TOPS Always Mean Better Performance?

Not necessarily. You can throw a high‑TOPS chip into a system and still end up with mediocre results if the rest of the setup isn’t optimized.

Sometimes, bottlenecks outside the chip matter more than the raw compute number. If the software stack isn’t tuned for the hardware, models can underperform. If I/O throughput is slow, say, a camera feed bottleneck or limited memory bandwidth, the chip spends more time waiting for data than processing it.

Latency

A chip might hit huge TOPS in batch processing, where it can work on large sets of data at once, but its architecture may not be optimized for single‑request, low‑latency inference. That means it can blaze through a thousand images in a lab test but hesitate when asked to process just one frame from a live video feed. In edge deployments, where decisions often need to be made in milliseconds, that kind of delay can be a deal‑breaker.

TOPS per watt is another big metric. In many edge scenarios, you can’t just plug into a wall socket with unlimited cooling. The most “powerful” chip on paper could actually be unusable if it drains a battery in minutes or overheats in an enclosed housing.

Then there’s thermal throttling. High‑performance processors can sustain big numbers for short bursts, but as heat builds, they slow down to protect themselves. In a fanless enclosure at the edge, sustained performance can look very different from peak performance.

This is why you sometimes see lower‑TOPS edge devices outperform bulkier, more powerful chips in specific workloads. It’s about finding a balance for the system as a whole.

Choosing the right AI hardware: How to think beyond TOPS

The smartest approach is to start with the workload, not the spec sheet. An AI chip that’s perfect for high‑volume image recognition in a warehouse might be the wrong choice for natural language processing at a call center.

Match the hardware to the job:

• Vision inference thrives on high‑throughput INT8 acceleration.
• Natural Language Processing (NLP) tasks often benefit from higher precision math and larger memory pools.
• Real‑time control systems demand ultra‑low latency even if total TOPS is modest.

Power constraints are huge in edge and embedded deployments. A fanless device in an outdoor kiosk has a very different thermal and power budget than a rack‑mounted server in a cooled closet. Sometimes, choosing a “slower” but more efficient chip means better performance over time.

Then there’s the ecosystem. A chip with massive TOPS but a weak developer toolkit can be painful to integrate. Libraries, framework compatibility, and vendor support often make the difference between a project that ships and one that stalls.

To balance performance, efficiency, and the right form factor for real‑world AI workloads, try SNUC’s NUC 15 Pro Cyber Canyon is a good example here. Powered by Intel’s latest Core Ultra processors combined with an iGPU and NPU, to deliver up to 99 TOPS for AI tasks in a compact, business‑ready design. It’s built to handle demanding inference workloads like vision recognition or object detection without sacrificing energy efficiency, making it well‑suited for edge deployments where both performance and size matter.

TOPS is just one piece of the puzzle

That big number on the spec sheet is a starting point, nothing more. TOPS tells you how much raw math a chip can push through under ideal conditions, but real‑world performance depends on the whole system; architecture, memory, software stack, power, and cooling.

The best AI hardware choice isn’t always the one with the highest TOPS. It’s the one that fits your workload, your environment, and your constraints. That might mean prioritizing TOPS per watt for an off‑grid sensor node, thermal stability for a factory floor, or software ecosystem maturity for a rapid development cycle.

If you’re weighing options, think beyond the number. Look at benchmarks that match your actual use case. Ask how the chip performs over time, not just in a burst. Factor in developer tools, compatibility, your environment, and the support you’ll get after deployment.

Because the truth is, in AI hardware, smart evaluation beats spec‑sheet bragging rights every single time!

To find the right TOPS for your project, contact us today.