Artificial intelligence (AI) has taken the world by storm and integration of AI accelerators and processors into applications is becoming more common. Still, a lot of myths exist about what they are, how they work, how they can enhance applications, and what is real versus hype.
1. GPUs are the best AI processors.
While GPUs have played a pivotal role in the realization of artificial intelligence (AI), and notwithstanding that they’re ubiquitous today, promoting them to be the "best" AI processors oversimplifies the evolving landscape of AI hardware.
GPUs are well-suited for large-scale model training where massive throughput — large memory capacities and high precision — are necessary to accurately process voluminous datasets. Drawbacks such as long processing time, possibly in the realm of months, low processing efficiencies, often in single digits, substantial energy consumption that poses constraints of cooling, and sizable latency are secondary concerns.
As the field matures, "best" is increasingly defined by application use modes and needs. GPUs were the right answer, until they weren’t the only one.
2. AI processors work equally well for training and inference.
It’s common to assume that any processor optimized for AI can seamlessly handle both training and inference. The reality is that training and inference have fundamentally different compute, efficiency, memory, latency, power, and precision requirements.
Designing a processor that excels at one doesn’t automatically mean it will perform well at the other. Each of the two deployment stages demands distinct computational goals and hardware needs. Training is about learning with precision and scale; inference is about speed, efficiency, and responsiveness.
Believing one chip can do both equally well leads to poor performance, inefficiencies, and missed optimization opportunities. The best systems separate the two — and optimize accordingly.
3. AI processors are only useful for data centers.
In the early days of AI deployment when model sizes required massive throughput, only cloud data centers had the compute infrastructure to train and run deep-learning models.
In today’s landscape, as inference becomes pervasive, AI processors are increasingly deployed across a wide range of environments beyond the data center, from edge devices and mobile phones to vehicles and industrial systems.
Today, AI processors are embedded in the devices around us, enabling smarter interactions, autonomous decision-making, and real-time processing where it's needed most. From cloud to edge, AI is now everywhere because that’s where the intelligence needs to be.
4. All AI processors can be used for general-purpose applications.
AI processors are specialized for distinctive AI tasks such as optimization of matrix/tensor operations. In contrast, general-purpose computing, like running a web browser, managing an OS, or performing file compression needs complex control flow, branching, and so forth.
In general, AI processor architectures don’t implement a full general-purpose instruction set architecture (ISA) or even a reduced instruction set architecture (RISC). Without a rich ISA and robust compiler support, they can’t efficiently handle non-AI applications. AI processors excel at what they're designed for, but they aren’t universal substitutes for general-purpose CPUs. Believing otherwise can lead to poor system design, wasted investment, and performance bottlenecks in non-AI applications.
5. More TOPS equate to better performance.
TOPS (teraoperations per second) is often used as a marketing metric for AI processors, but it doesn’t reflect real-world performance. While it measures the theoretical peak throughput under ideal conditions (e.g., 100% utilization of all compute units) of an AI chip, it says nothing about how efficiently that performance is utilized in actual workloads. TOPS can be inflated by using lower-precision operations (e.g., INT4 or INT8 instead of FP16 or FP32).
A chip may have high TOPS, but if data can't reach the compute units quickly, the TOPS potential is wasted. Furthermore, an architecture may have massive compute potential but underperform if the software ecosystem is immature or poorly tuned.
Finally, different AI tasks require different characteristics. Vision models may benefit from high parallelism (where TOPS helps), but generative transformers require high memory throughput, cache coherence, and data reuse — not raw TOPS.
TOPS is a theoretical ceiling, not a performance guarantee. It’s like judging a car by its top speed without considering road conditions, fuel efficiency, or handling. Real AI performance is dictated by architecture balance, software stack, data movement efficiency, and model compatibility, not just raw compute numbers.
6. Bigger chips with more cores always perform better.
At first glance, a larger chip with more processing cores seems like it should deliver better performance. In reality, scaling up silicon area and core count introduces major diminishing returns and, in many cases, even degrades performance, efficiency, or usability.
AI workloads don't always scale linearly with core count. Larger chips need more memory bandwidth to feed their compute units and require longer wires and more complex interconnects. This leads to routing congestion and higher energy consumption.
Performance doesn’t scale linearly with chip size or core count. Bigger chips introduce engineering, architectural, and economic tradeoffs that can negate their theoretical advantage.
In AI hardware, efficiency, data movement, software optimization, and task alignment often outperform raw size. The best chip isn’t the biggest; rather, it’s the one most balanced for the job.
7. FP32 is the gold standard for AI compute.
In the early days of the training and inference in deep learning, FP32 (32-bit floating point) was the default format. As AI technology evolved, AI workloads abandoned FP32 in favor of lower-precision formats like FP16, INT16, or INT8.
The belief that FP32 is still the gold standard overlooks massive improvements in efficiency, performance, and accuracy using lower-precision alternatives. In fact, lower precision can match or exceed FP32 accuracy through techniques like quantization-aware training and mixed-precision training. Models can often maintain virtually identical accuracy with FP16 or FP8.
So, FP32 is no longer the gold standard. And today’s trend is shifting away from INT in favor of FP, with some even advocating FP4.
AI computing relies on precision optimization, not maximum bit-width. The best performance and efficiency come from choosing the right precision for the task, not the most precise format available.
8. Processing in sparsity mode takes precedence over density mode
Sparse computation may appear to be advantageous vis-a-vis dense elaboration. It avoids processing zero-valued elements in tensors (weights, activations, or even data) to reduce compute, memory, and power consumption and improve efficiency without sacrificing model accuracy.
The fact is, sparsity mode is highly dependent on the model structure, data patterns, and hardware capabilities. Sparsity isn’t a one-size-fits-all optimization and doesn’t universally outperform dense computation. Stated simply, it’s a conditional optimization.
Dense mode remains the default in many cases because it’s mature, predictable, and broadly compatible. Sparsity is a powerful tool, but only in the right context with the right support.
9. Efficient scalar computing is all that’s needed for AI processing.
Scalar computing, defined as processing one operation at a time on single data elements, plays an important role in control logic and orchestration. However, it falls far short of meeting the performance and efficiency demands of modern AI workloads.
While scalar computing is necessary, it’s not sufficient for AI processing. The demands of AI require parallel, vectorized, and matrix-accelerated computation best handled by custom hardware designed for massive, concurrent workloads.
10. Processing efficiency can be achieved solely via advanced chiplet architecture.
Chiplet-based design offers several benefits. Among them, it ensures higher yield and lower cost since smaller dies are easier to manufacture. It results in modular scalability by enabling mix-and-match functions like CPUs, GPUs and accelerators. It also distributes heat and power more efficiently across the assembly. The cumulative advantages often create the impression that efficiency, especially in performance-per-watt, is a built-in outcome.
While chiplet technology is a valuable tool for scalability and integration, true processing efficiency requires a fundamentally new hardware/software architecture engineered for AI workloads. This blueprint ought to include an innovative memory architecture to overcome the memory wall, dynamically reconfigurable compute cores tailored to the algorithmic demands of AI applications, and an overarching design aimed at simplifying the software stack.
Short of the above, chiplets alone can’t deliver the expected gains.
11. CUDA is the reference software for AI processors.
Although NVIDIA’s Compute Unified Device Architecture (CUDA) has become a dominant standard for AI development, it’s not a universal reference. Believing that CUDA is the benchmark or required interface for all AI processors ignores the diversity of hardware architectures and software ecosystems emerging today.
CUDA is the dominant tool for one vendor’s ecosystem. The broader AI industry is evolving toward open, flexible, and hardware-independent software frameworks. CUDA remains important, but its ascendency is increasingly challenged by the need for portability, interoperability, and freedom of hardware choice. The future of AI isn’t tied to one software design kit (SDK). Rather it’s multilingual, open source, and platform-aware.
