Is one kind of AI processor likely to reign supreme?
GPUs have long been the workhorse behind much of the AI infrastructure buildup. But as AI needs have grown, specialized hardware has started to emerge. This is true across the ecosystem, from training to inference, and hyperscalers and smaller outfits alike all have slightly different approaches to their cloud-based AI needs.
As specialized hardware has become more common, of course, the differences between the kinds of semiconductors used for cloud AI have gotten a little muddled and confusing. GPUs, NPUs, ASICs, and FPGAs all have their uses, and there’s plenty of crossover between them. But each is optimized for a different balance of throughput, flexibility, and power efficiency.
Here’s a look at the different types of AI processing semiconductor and what their major differences are.
GPU
Pros:
- Excellent parallel processing power for training large AI models
- Highly versatile and supports many AI frameworks
- Backed by mature software ecosystems like Nvidia CUDA
Cons:
- Less efficient for some workloads than others
- High power consumption and heat output
- Expensive to scale at data-center level
- Performance bottlenecks can arise from memory bandwidth and interconnect limitations
Graphics processing units or GPUs have long been the backbone of AI data centers. GPUs, as you might expect, were originally designed specifically for graphics rendering, but their parallel architecture ended up being perfect for training neural networks.
GPUs use thousands of parallel cores built on a SIMD architecture to perform large-scale matrix operations efficiently. Paired with HBM memory, they deliver high bandwidth for AI training. Interconnects like NVLink enable multi-GPU scaling across racks, while software stacks such as CUDA and ROCm support major AI frameworks like PyTorch and TensorFlow.
Slowly but surely, companies like Nvidia and AMD have started building specialized GPUs specifically for AI. These include the likes of the Nvidia H200 and AMD Instinct MI450. Actual graphics-focused GPUs, like Nvidia’s GeForce RTX 5090, are still often used for AI workflows too, however most of the larger AI outfits and hyperscalers focus more on AI-specific GPUs, due to their high-bandwidth memory, interconnect technology, and more. The strength of these specialized GPUs lies in versatility – developers can run an array of AI frameworks on them, and in the case of Nvidia hardware for example, leverage the still unmatched CUDA ecosystem.
NPU
Pros:
- Extremely power-efficient for inference tasks
- Purpose-built for neural network operations
- Enables low-latency performance for edge and on-device AI
Cons:
- Limited flexibility compared to GPUs
- Typically focused on inference rather than training
- Ecosystem and framework support still developing
Neural processing units, or NPUs, are specialized processors designed to accelerate neural network inference. While originally developed for smartphones and edge devices, NPUs are increasingly finding their way into data-center environments as inference demands grow.
NPUs use matrix multiplication arrays and on-chip SRAM for fast, low-power inference. They rely on low-precision formats like INT8 or BF16 to boost efficiency and minimize memory movement. In data centers, NPUs integrate alongside CPUs or GPUs to accelerate transformer-based inference at a fraction of the power cost. They’re optimized for running pre-trained models, not training them from scratch, and can deliver substantial energy savings in large-scale inference workloads. Intel, for example, integrates NPU blocks into its Xeon processors to offload AI tasks.
The main trade-off is flexibility. NPUs excel at specific matrix and tensor operations but can’t match the broad programmability of GPUs. Still, as inference workloads scale, NPUs are becoming more useful in balancing performance per watt across AI infrastructure.
ASIC
Pros:
- Unmatched performance and efficiency for targeted workloads
- Highly optimized for specific AI models or applications
- Reduced latency and power consumption at massive scale
Cons:
- Completely inflexible and can’t be reprogrammed post-fabrication
- Long and expensive design cycles
- Only viable for organizations with huge, stable AI workloads
Application-specific integrated circuits, or ASICs, take specialization to the extreme. Rather than being general-purpose like GPUs, ASICs are custom silicon built for one narrow purpose – and are often used in a single AI workload or framework. For that reason, ASICs are usually only an option for hyperscalers or others with the financial ability to design chips specific to their workloads, like OpenAI, which is building new ASICs with Broadcom.
ASICs use fixed-function logic and systolic arrays tailored to specific AI workloads, delivering extremely high FLOPS-per-watt efficiency. Their tight coupling of compute and HBM maximizes throughput. In hyperscaler data centers, ASICs are how companies maximize throughput and efficiency at scale. Amazon has Inferentia and Trainium chips, Google uses TPUs (which are actually just ASICs themselves), and Meta’s MTIA accelerators are purpose-built for inference in recommendation engines.
The advantage is clear. ASICs eliminate the overhead of flexibility, dedicating all silicon resources to a known task. The downside is that any change in model architecture or framework could render a chip partially obsolete. As a result, ASICs make sense for hyperscalers that control their entire software stack, but not for smaller operators or fast-moving research environments, where flexibility is much more important.
FPGA
Pros:
- Reconfigurable after deployment
- Low-latency, deterministic performance for streaming data
- Useful for AI inference, networking, and pre/post-processing
Cons:
- Steeper programming complexity
- Lower raw throughput compared to GPUs and ASICs
- Not ideal for training large models
Field-programmable gate arrays, or FPGAs, occupy a unique niche between flexibility and specialization. Unlike ASICs, FPGAs can be reprogrammed after manufacturing, allowing them to adapt to new model architectures or workloads without new silicon.
FPGAs use reconfigurable logic blocks and DSP slices that can be rewired for different workloads, offering flexibility close to software with near-hardware speed. They’re ideal for low-latency inference and network acceleration. FPGAs often serve as inference accelerators or networking offload engines, where latency and adaptability matter more than sheer compute throughput. Microsoft, for instance, uses FPGAs in its Project Catapult infrastructure to accelerate search and Azure workloads.
While they lack the raw performance of GPUs or TPUs, FPGAs excel in environments where workloads evolve rapidly or require tight integration with networking and I/O. They’re less common for model training but remain valuable for inference pipelines, especially where determinism and customizability are priorities.
Conclusions
Over time, GPUs are likely to remain the dominant force in AI infrastructure thanks to their flexibility, ecosystem maturity, and ongoing architectural improvements. However, as energy efficiency and specialization become more critical, ASICs will continue to gain ground among hyperscalers that can afford to design custom silicon for tightly defined workloads. NPUs will grow in relevance at the inference layer, especially in edge and hybrid environments, where cost and power efficiency matter most. FPGAs may gradually narrow to niche roles in networking and adaptive inference, where reconfigurability still offers a distinct advantage.