A practical guide to AI accelerators

AI hardware has acquired enough acronyms to make a simple question unnecessarily difficult.

GPU. NPU. TPU. FPGA. ASIC. AI accelerator. AI chip.

The names are often presented as if they describe cleanly separated alternatives. They do not.

A GPU is an AI accelerator when it is used to accelerate AI. Google's TPU is a type of ASIC. An NPU can itself be implemented as fixed silicon or even as programmable IP inside an FPGA or adaptive system. AMD's current Vitis AI platform, for example, implements its NPU using programmable logic and AI Engine resources on adaptive hardware.

So a list that asks you to choose between “GPU, NPU, TPU, FPGA, or ASIC” is useful for discussing hardware families, but it is not a strict taxonomy.

The more useful distinction is how specialized the hardware is, what work it accelerates, what software it requires, and how much freedom you retain when the workload changes.

That leads to a better question:

Which AI accelerator fits the model and deployment you actually have?

What is an AI accelerator?

An AI accelerator is hardware designed to speed up computational operations used by artificial intelligence and machine learning workloads.

Most modern neural networks spend a great deal of time performing highly parallel numerical operations such as matrix multiplication and multiply-accumulate operations. General-purpose CPUs can perform this work, but processors designed around parallel or matrix-heavy computation can usually perform far more of it at once.

AI accelerators therefore move some of the computational burden away from the CPU and onto hardware better suited to the job.

The term covers several types of hardware:

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The categories are deliberately imperfect.

A TPU is an ASIC. Many NPUs are ASICs too. GPUs contain specialized AI hardware such as Tensor Cores while remaining programmable enough to handle many workloads outside machine learning. NVIDIA's CUDA documentation describes GPUs as general parallel computing devices used across AI, scientific computing, analytics, and other compute-intensive applications. (NVIDIA CUDA Programming Guide)

The hardware landscape is better understood as a spectrum from general-purpose flexibility toward workload-specific efficiency.

Why AI needs accelerators

A modern neural network can require enormous numbers of relatively simple numerical operations.

Consider matrix multiplication. Instead of solving one difficult calculation sequentially, the processor performs huge numbers of multiply-and-add operations across arrays of values.

This kind of work rewards parallel hardware.

A CPU devotes substantial silicon to things such as caches, branch prediction, control flow, and low-latency general-purpose execution. Those features are useful because a CPU needs to handle unpredictable software.

A GPU makes a different trade-off. It dedicates much more hardware to processing many operations in parallel. NVIDIA's current CUDA documentation describes GPUs as being designed to execute thousands of threads concurrently rather than optimizing primarily for the fastest possible execution of individual serial threads.

AI-specific accelerators can narrow the problem further.

Google's TPUs, for example, contain matrix-multiply units built from large arrays of multiply-accumulate units. Google describes them as application-specific integrated circuits designed specifically to accelerate machine-learning workloads. (Google Cloud TPU architecture)

The further the hardware specializes, the less silicon and energy it may need to spend on capabilities that the target workload does not use.

The catch is that the workload has to match those assumptions.

The most important distinction is specialization versus flexibility

Imagine two processors.

One can run almost any parallel workload you give it. It supports several frameworks, custom kernels, changing model architectures, rendering, simulation, and general numerical computing.

The other is designed around a smaller set of neural-network operations and numerical formats. Your model happens to fit those operations perfectly.

The second processor may execute that workload more efficiently.

Then you change the model.

Perhaps it introduces an unsupported operator. Perhaps you need a different numerical format. Maybe you move from inference to fine-tuning, add a custom kernel, or need much more memory.

The flexible processor still works.

The specialized processor may require a new compiler path, model conversion, CPU fallback, a software update, or completely different hardware.

That trade-off appears repeatedly across AI accelerators.

It is also why hardware specifications should never be evaluated without the software stack around them.

GPUs as AI accelerators

GPUs became central to modern AI because neural networks and graphics share an important computational trait: enormous amounts of parallel arithmetic.

The GPU evolved from graphics-specific hardware into a programmable parallel processor. CUDA, introduced by NVIDIA in 2006, made it possible to use GPU throughput without going through graphics APIs, and the surrounding software stack has since expanded into libraries and tools for deep learning, scientific computing, simulation, analytics, and other workloads. (CUDA Programming Guide)

Modern NVIDIA GPUs also contain Tensor Cores designed specifically to accelerate matrix operations at different numerical precisions. That means the GPU itself now combines relatively general parallel compute with more specialized AI hardware. (NVIDIA Tensor Cores)

GPUs are strong when the workload changes

The major advantage of a GPU is not that it always wins every performance comparison.

It is that a GPU can usually do many different jobs.

You can:

• train a neural network;
• fine-tune an LLM;
• serve inference;
• write custom kernels;
• test new architectures;
• run computer vision;
• render images;
• perform scientific simulation;
• process video;
• switch frameworks;
• run several models on the same infrastructure.

That flexibility matters most during research, development, and any production environment where models change frequently.

It explains why GPUs remain important even as more specialized AI chips appear.

Our article on why GPUs aren't really “graphics cards” anymore explores that shift from graphics processor to general compute accelerator.

Where GPUs give something up

Flexibility costs silicon and energy.

A GPU includes hardware and programmability that a narrow inference workload may never use. A purpose-built accelerator designed around one class of models may therefore achieve better performance per watt or better economics for that specific workload.

That does not make the GPU inefficient in general. Efficiency has to be measured against completed work.

A GPU drawing more power can still consume less energy per completed task if it finishes the job much faster. This distinction becomes important when comparing AI accelerators with GPUs for production inference.

NPUs as AI accelerators

A neural processing unit narrows the processor around neural-network workloads.

On modern PCs, an NPU often exists alongside the CPU and GPU and handles sustained local AI tasks without requiring the more power-hungry processors to remain heavily active.

That can make an NPU useful for speech processing, camera effects, computer vision, transcription, image enhancement, and supported local generative AI.

But, as we covered in what an NPU is and what it is good at, NPU does not describe one performance class.

The integrated NPU inside a laptop and a data-center NPU system have different memory architectures, power budgets, software environments, and deployment goals.

Even programmable hardware complicates the definition. AMD's Vitis AI platform now provides NPU IP for Versal AI Edge adaptive devices, using programmable logic and AI Engine arrays to implement inference acceleration. (AMD Vitis AI)

This is another reason not to treat “NPU” and “FPGA” as universally exclusive categories.

When an NPU makes sense

An NPU becomes especially attractive when:

• the model is supported;
• the workload runs frequently;
• power matters;
• local execution matters;
• latency needs to be predictable;
• the model changes relatively little;
• the software stack is already mature for the device.

A laptop running continuous audio processing presents a different hardware problem from a research lab training a transformer.

The first may benefit enormously from a small NPU.

The second probably values programmability and memory far more.

For the direct comparison, see NPU vs GPU for AI workloads.

TPUs as AI accelerators

A TPU, or Tensor Processing Unit, is Google's family of custom machine-learning accelerators.

Unlike GPU and NPU, TPU is a Google-specific product name rather than a general processor category.

Google explicitly describes Cloud TPUs as application-specific integrated circuits, or ASICs, designed to accelerate machine-learning workloads. Current TPU systems can be used through Compute Engine, Google Kubernetes Engine, and Vertex AI. (Cloud TPU documentation)

Their architecture is built heavily around matrix processing.

Current TPU TensorCores contain matrix-multiply units made from systolic arrays of multiply-accumulate units. Google also supports ML frameworks including JAX and PyTorch on TPU hardware.

When TPUs fit well

Google's own guidance points toward workloads dominated by large matrix operations, large models and batches, long-running training jobs, and workloads that fit the supported TPU compilation environment. (Introduction to Cloud TPU)

TPUs can support both training and inference.

Current Google Cloud infrastructure includes large multi-chip TPU configurations as well as systems intended for inference workloads.

The trade-off is tighter coupling to the TPU environment.

Code intended to execute on TPUs is compiled through XLA, and hardware availability is naturally tied to Google's infrastructure.

That may be entirely reasonable when the workload fits.

It is less attractive if portability between several hardware providers is a major requirement.

FPGAs and adaptive hardware for AI

An FPGA, or field-programmable gate array, is fundamentally different from a fixed processor.

Its hardware logic can be reconfigured after manufacturing.

Instead of accepting the processor architecture that came from the factory, developers can create hardware pipelines tailored to the application.

That can be valuable when a workload needs:

• predictable latency;
• custom data paths;
• specialized preprocessing;
• unusual numerical formats;
• direct sensor integration;
• tightly controlled edge execution.

Modern adaptive systems increasingly blur the boundary between FPGA, dedicated AI engine, CPU, and NPU.

AMD's current Vitis AI platform combines programmable logic with dedicated AI Engine resources and NPU IP to run neural-network inference on its adaptive SoCs. (AMD Vitis AI Developer Hub)

This flexibility operates at a different layer from GPU programmability.

A GPU programmer usually writes software for an existing processor architecture.

An FPGA developer can change portions of the effective hardware architecture itself.

That power comes with a development cost. FPGA workflows can require deeper hardware knowledge, synthesis, timing analysis, hardware/software co-design, and platform-specific tooling.

For a standardized web application that changes models every month, that work may make little sense.

For a high-volume embedded system with strict latency and power requirements, it may be exactly the right trade-off.

This becomes particularly relevant in edge AI hardware.

ASICs and purpose-built AI chips

ASIC stands for application-specific integrated circuit.

Unlike an FPGA, whose logic can be reconfigured after manufacturing, an ASIC implements its major hardware design permanently in silicon.

That makes ASICs potentially efficient because the chip can be built around the exact workload designers expect it to perform.

It also makes “ASIC vs TPU” or “ASIC vs NPU” slightly misleading.

A TPU is an ASIC. Many dedicated NPU designs are ASICs. ASIC describes how specialized fixed hardware is built, while TPU and NPU describe particular purposes or product families.

Several cloud providers now design their own AI ASICs.

Google has TPUs. AWS offers purpose-built Trainium and Inferentia chips. AWS currently positions Trainium across training and inference, while Inferentia is designed specifically around inference workloads. Both use AWS's Neuron software stack and integrate with frameworks and tools including PyTorch, JAX, Hugging Face, and vLLM. (AWS AI chips)

This shows the central appeal of purpose-built AI silicon.

If an infrastructure provider knows that enormous amounts of machine-learning work will run on its platform, designing hardware around those workloads can potentially produce better economics than buying a completely general accelerator for every job.

But the software environment becomes part of the product.

You are choosing the compiler, runtime, libraries, hosting environment, model support, and migration path alongside the chip.

AI accelerator vs GPU is technically the wrong comparison

Search for AI hardware and you will frequently encounter the phrase AI accelerator vs GPU.

Technically, a GPU can be an AI accelerator.

What people usually mean is:

Should I use a GPU or a more specialized AI accelerator?

That is a useful comparison.

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The crucial word in the second column is potentially.

More specialized hardware is not automatically faster, cheaper, or greener.

It has to execute the actual workload well.

AI accelerator cards are a form factor, not another processor type

Another common search term is AI accelerator card.

An accelerator card is hardware that adds acceleration to an existing system, usually through an expansion interface such as PCI Express.

A card might contain:

• a GPU;
• an NPU;
• an FPGA;
• a custom ASIC;
• several accelerator chips.

NVIDIA's L4, for example, is a low-profile PCIe GPU used for AI, video, graphics, and other workloads. It is simultaneously a GPU, an AI accelerator, and an accelerator card. (NVIDIA L4)

Smaller edge accelerators may instead use M.2, mini-PCIe, USB, or custom module formats.

The form factor tells you how the device connects to the system.

It does not tell you what architecture is inside.

CPU vs AI accelerator

The CPU has not disappeared from AI systems.

Almost every accelerator still works as part of a larger heterogeneous computer.

The CPU may:

• load the application;
• tokenize input;
• prepare data;
• run business logic;
• handle networking;
• schedule work;
• process unsupported operations;
• control the accelerator;
• perform pre- and postprocessing.

NVIDIA's current CUDA model explicitly describes GPU applications as heterogeneous systems in which the host CPU starts execution and coordinates work running on the GPU.

Some AI workloads do not need an accelerator at all.

A small model, low request volume, preprocessing pipeline, embedding job, or application dominated by ordinary program logic may run perfectly well on a CPU.

That is why our broader CPU vs GPU vs NPU guide begins with the workload rather than assuming that every AI application requires specialized hardware.

Training and inference want different things

One of the most important hardware distinctions has nothing to do with processor names.

It is the difference between training and inference.

Training adjusts a model's parameters. It requires forward and backward passes, gradient calculation, parameter updates, and potentially enormous amounts of temporary state.

Inference takes a trained model and uses it to generate predictions or outputs.

That changes the hardware problem.

Training tends to reward:

• large memory capacity;
• high memory bandwidth;
• fast accelerator interconnects;
• distributed computation;
• support for several numerical formats;
• flexible software;
• mature debugging and profiling.

Inference can place more emphasis on:

• latency;
• throughput;
• batch efficiency;
• memory per request;
• low-precision execution;
• power efficiency;
• cost per output.

A processor that is excellent at one does not automatically dominate the other.

AWS makes the distinction visible in its own accelerator strategy: Inferentia was designed around inference, while Trainium is purpose-built for larger-scale AI training and now spans training and inference workloads.

We examine these differences directly in training vs inference hardware.

For production LLMs, our existing practical guide to LLM inference also covers the serving side of the problem.

Precision changes the hardware equation

AI workloads increasingly use numerical formats smaller than traditional FP32.

Training may use BF16, FP16, FP8, or mixed precision.

Inference can move toward INT8, INT4, FP4, or other compact formats when the model and hardware support them.

Lower precision can reduce memory requirements, decrease memory traffic, and allow hardware to perform more operations in parallel.

But a format only helps when the accelerator actually implements it efficiently.

This is another reason peak specifications are dangerous.

A chip might advertise enormous INT8 throughput while your workload requires BF16. Another might support FP4 but only through a particular runtime and model path.

The model and software determine which hardware capability you can actually use.

Our LLM quantization guide explains why reducing bit width primarily changes capacity and data movement, while real speedups still depend on kernels, hardware, and workload shape.

Memory can matter more than arithmetic

Accelerator comparisons tend to lead with compute.

For large AI models, memory can decide the result first.

The accelerator needs space for model weights. Inference adds runtime buffers and, for transformer models, KV-cache state. Training can require gradients, optimizer states, activations, and other temporary data.

If the model does not fit, theoretical arithmetic throughput stops being particularly interesting.

Data movement matters too.

A processor may execute matrix multiplication extremely quickly while spending much of its time waiting for weights or activations to arrive.

Modern accelerator designs therefore combine compute with different memory architectures: discrete GPU VRAM, high-bandwidth memory, shared system memory, on-chip SRAM, local FPGA memory, and multi-accelerator interconnects.

This is why choosing AI hardware from a TOPS or FLOPS number alone is rarely defensible.

TOPS and FLOPS do not tell you which accelerator wins

Hardware vendors need concise specifications.

Real workloads are not concise.

TOPS measures trillions of operations per second. FLOPS measures floating-point operations per second. Both can describe theoretical arithmetic capability under defined conditions.

Neither tells you how quickly your application will finish.

You also need to know:

• precision;
• model architecture;
• memory capacity;
• memory bandwidth;
• batch size;
• sequence length;
• operator support;
• compiler quality;
• framework overhead;
• utilization;
• latency target;
• interconnect performance;
• fallback behavior.

Two accelerators can quote similar peak arithmetic performance and deliver completely different application performance.

The reverse is also possible.

Our guide to TOPS vs FLOPS and AI performance metrics deals specifically with this problem.

For comparative testing, workload benchmarks are much more informative. MLPerf Inference, for example, defines complete workloads covering LLMs, image generation, object detection, recommendation, and other tasks rather than ranking accelerators by one theoretical peak figure. (MLPerf Inference)

Software support is part of the accelerator

When choosing AI hardware, developers often compare the chip first and software second.

In practice, the two cannot be separated.

NVIDIA GPUs come with CUDA and a large collection of accelerated libraries.

Google TPUs rely on the TPU environment and XLA compilation.

AWS Trainium and Inferentia use Neuron.

AMD adaptive accelerators use Vitis AI.

Different NPU vendors expose different runtimes and execution providers.

Those stacks determine which models run, how they are compiled, which operators are available, what numerical formats work, and how easily the workload can be debugged.

Google's own TPU guidance illustrates the issue. It recommends GPUs rather than TPUs for workloads containing unsupported operations or significant custom operations that do not map cleanly onto TPU execution.

That is a useful lesson beyond Google.

An accelerator that does not support your model is not an acceleration strategy.

Edge AI changes the priorities

A data center and a camera mounted inside a factory should not use the same decision matrix.

At the edge, power, cooling, physical space, connectivity, privacy, and deterministic latency can become more important than maximum throughput.

An embedded system may need to process a camera stream continuously without uploading it anywhere.

A laptop may need to transcribe audio for hours without destroying battery life.

A robot may need millisecond-level responses even when the network is unavailable.

Those conditions favor hardware designed around efficient local execution.

NPUs, FPGAs, adaptive SoCs, small GPUs, and dedicated accelerator modules can all appear in this space.

The choice depends on how fixed the model is, how much power is available, what software must run locally, and whether the hardware needs to support other workloads.

We treat that as its own deployment problem in our guide to edge AI hardware.

Data-center AI changes them again

Move the same model into a data center and the constraints shift.

Power still matters, but now it may be measured against thousands or millions of requests.

Networking matters.

Accelerator-to-accelerator communication matters.

Memory capacity matters.

Utilization matters.

The software team's time matters.

A purpose-built accelerator that saves a fraction of the cost per inference can become economically significant at enormous scale.

But specialization also introduces migration and operational costs.

If a team changes models every month, uses several frameworks, writes custom kernels, or serves unpredictable workloads, the flexibility of GPUs may be worth more than a theoretical efficiency advantage elsewhere.

This is why cost should be calculated around the complete workload, rather than the purchase price or hourly rate of the chip.

How to choose an AI accelerator

Start with the application and eliminate hardware that cannot meet its requirements.

1. Decide whether you are training or running inference

The requirements differ enough that this should be the first branch in the decision.

If you do both, prioritize the stage that dominates your compute cost or decide whether separate hardware makes sense.

2. Identify the model

Ask:

• What architecture is it?
• How many parameters?
• What numerical precision?
• Which operators does it use?
• Can it be quantized?
• How much memory does it require?
• How often will it change?

A fixed vision model and a research pipeline testing experimental transformers should not lead to the same hardware choice.

3. Check software compatibility

Before reading benchmark charts, verify that the runtime and framework support the accelerator.

Look for the exact model architecture and important operators.

Check what happens when something is unsupported.

4. Work out the memory requirement

Include the complete workload, not just model weights.

For LLM inference that may include KV cache, runtime buffers, batching overhead, and several concurrent requests.

5. Define latency and throughput

“Fast” is not a requirement.

Do you need one response in 20 milliseconds?

Ten thousand predictions per second?

A thousand concurrent LLM users?

Maximum training throughput over six weeks?

Different answers favor different architectures.

6. Define the power and deployment constraints

A laptop has a battery.

An edge box has a thermal envelope.

A cloud deployment has an energy bill and utilization target.

A research workstation may care much less about power than flexibility.

7. Include engineering cost

Ask how much work it takes to:

• port the model;
• compile it;
• optimize it;
• maintain it;
• debug it;
• migrate when the model changes;
• train developers on the stack.

Hardware that saves $5,000 while consuming three months of engineering time is not automatically cheaper.

8. Benchmark the real workload

Use the intended model, precision, context length, batch size, concurrency, runtime, and quality requirements.

Then compare:

• latency;
• throughput;
• memory use;
• power;
• cost;
• development effort.

Anything else remains an estimate.

AI accelerator decision matrix

Here is a better version of the simple “NPU or GPU?” table that inspired this series.

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The starting point is deliberately not a final answer.

Hardware should survive the benchmark before it survives procurement.

When cloud GPU access is the practical choice

Purpose-built AI hardware makes a great deal of sense in the right environment.

If you manufacture millions of devices running the same model, integrating specialized silicon can be rational.

If you operate an enormous inference service with stable model requirements, designing or committing to a specialized accelerator stack can also pay off.

Most teams are dealing with a messier reality.

The model is still changing.

Someone wants to try a different quantization method.

A new open model arrives next week.

The application suddenly needs twice the context length.

A research team wants custom PyTorch code.

Production needs one architecture while development needs another.

In those situations, flexibility has economic value.

Compute with Hivenet provides GPU and CPU infrastructure for teams that want control over their own environment, libraries, runtime, and serving stack. Current Compute workloads include model inference, development, fine-tuning, rendering, batch jobs, notebooks, and general GPU or CPU compute.

Hivenet currently uses RTX-class GPU infrastructure rather than asking users to commit to a proprietary AI accelerator stack. The RTX 5090, for example, provides 32 GB of GPU memory on current Compute configurations, and Hivenet has published real inference benchmarks from RTX 5090 workloads.

If the infrastructure itself is not something you want to operate, the Hivenet Inference API provides the other path: dedicated OpenAI-compatible model endpoints with the serving layer managed for you.

The point is not that GPUs beat specialized accelerators.

It is that hardware flexibility can be valuable while the workload is still changing.

FAQ about AI accelerators

What is an AI accelerator?

An AI accelerator is hardware designed to speed up computational operations used by artificial intelligence and machine-learning workloads. GPUs, NPUs, TPUs, FPGAs, and purpose-built AI ASICs can all act as AI accelerators.

Is a GPU an AI accelerator?

Yes. A GPU used for machine learning is an AI accelerator. Modern GPUs also contain dedicated matrix-processing hardware designed specifically for AI workloads.

Is an AI accelerator better than a GPU?

The question usually means whether a specialized AI accelerator is better than a GPU. A specialized accelerator can be more efficient for a compatible, stable workload. GPUs generally provide greater flexibility across models, frameworks, training, inference, and non-AI compute.

What is an AI accelerator card?

An AI accelerator card is an expansion card containing hardware that accelerates AI workloads. It may contain a GPU, NPU, FPGA, ASIC, or another processor. PCIe is a common form factor, while smaller edge accelerators may use M.2 or other interfaces.

What is the difference between an NPU and an AI accelerator?

An NPU is one type of AI accelerator. The broader AI accelerator category also includes GPUs, TPUs, FPGAs, and other purpose-built chips.

What is the difference between a TPU and an ASIC?

A TPU is a type of ASIC designed by Google for machine-learning workloads. ASIC describes the broader class of application-specific integrated circuits; TPU is Google's particular accelerator family.

Is an FPGA an AI accelerator?

An FPGA can be used as an AI accelerator when its programmable hardware is configured to accelerate neural-network or other machine-learning operations. Modern adaptive devices can combine programmable logic with dedicated AI engines and NPU IP.

Which AI accelerator is best for LLMs?

There is no universal winner. GPUs provide broad support and flexibility. TPUs and purpose-built ASICs can be compelling at large scale when the model fits their software environment. Client NPUs may run smaller local LLMs. The right hardware depends on model size, memory, precision, context, throughput, latency, software support, and deployment scale.

Which AI accelerator is best for inference?

For frequently changing models, GPUs are a strong general starting point. A specialized accelerator can become attractive for stable, high-volume inference when its software supports the model and benchmarks show better economics. See our deeper guide to AI accelerators vs GPUs for inference.

Which AI accelerator is best for training?

GPUs remain a flexible starting point for most model development and training. TPUs and other training-oriented ASICs can be strong alternatives for supported large-scale workloads. The right answer depends on model architecture, software, memory, interconnects, precision, and scale.

Do I need an AI accelerator?

Not always. CPUs can handle many small models and low-volume workloads. An accelerator becomes useful when parallel AI computation is large enough for the additional hardware and software complexity to provide a meaningful performance or efficiency gain.

Choose the workload before you choose the acronym

AI hardware categories are useful until they become shortcuts.

GPU does not mean “training chip.”

NPU does not mean “small inference chip.”

TPU is not a generic category parallel to ASIC.

FPGA does not tell you what accelerator has been implemented inside it.

And “AI accelerator” does not describe something separate from a GPU.

The processors overlap because modern systems overlap.

What matters is the work.

Start with the model. Decide whether you are training or serving it. Determine the memory requirement, numerical precision, latency, throughput, deployment environment, power budget, and software stack.

Then ask how specialized the hardware can safely become.

If you need broad compatibility, frequent experimentation, and the ability to change workloads, a GPU is difficult to displace.

If the workload becomes predictable enough, specialization starts to become more attractive.

That is the pattern that connects this entire series.

Read NPU vs GPU for AI workloads, what an NPU is, CPU vs GPU vs NPU, AI accelerators vs GPUs for inference, edge AI hardware, TOPS vs FLOPS, and training vs inference hardware to follow each decision further.

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