Why are GPUs still king of AI?

GPUs keep winning in AI not because they’re “perfect,” but because they hit a rare combination: high throughput, strong software support, flexible programmability, and a supply chain that can actually deliver millions of chips into real systems. Custom accelerators and NPUs can outperform GPUs on specific workloads, yet they often struggle to match the broad usefulness and frictionless adoption that make GPUs the default choice for training and increasingly for inference.

The workload favors brute-force parallelism

Modern AI—especially deep learning—leans heavily on dense linear algebra: matrix multiplies, convolutions, attention blocks, and vector operations. These tasks are massively parallel, and GPUs were built for massive parallelism long before AI became mainstream. Thousands of lightweight cores, wide memory interfaces, and hardware scheduling let GPUs push enormous floating-point and low-precision math throughput.

More importantly, GPUs are good at the “messy middle” of AI workloads. Training isn’t just one giant matrix multiply. It’s kernels chained together with data movement, activation functions, normalization, optimizer steps, embedding lookups, and a growing list of custom ops. GPUs handle this mix reasonably well without needing the model to fit a narrow template.

GPUs are general enough to stay useful

A key reason GPUs remain dominant is that they’re programmable in a fairly general way. When model architectures shift—CNNs to transformers, transformers to mixture-of-experts, diffusion models, multimodal pipelines—GPUs can usually adapt through new kernels and compiler improvements without requiring new silicon.

This flexibility matters because AI workloads change faster than chip design cycles. A custom accelerator designed around one era’s “hot operator” can look outdated when training practices shift (new attention variants, quantization schemes, sparsity patterns, routing, or memory-saving tricks). GPUs, while not always optimal, remain good enough across generations of model design.

The software moat is real

Hardware performance only matters if developers can access it easily. GPUs benefit from years of investment in compilers, libraries, kernel fusion, profiling tools, debuggers, and a culture of optimization. That “software moat” reduces time-to-results:

• Researchers can prototype quickly using mature frameworks and stable drivers.
• Production teams can tune bottlenecks with widely known tools and patterns.
• Vendors ship optimized libraries for common ops, and the community fills gaps fast.

For many teams, the most expensive part of AI isn’t the chip—it’s engineering time. GPUs reduce that cost because the path from model code to running system is well-paved.

Memory bandwidth and interconnects match AI’s hunger

Training large models is frequently memory-bound. You need to move enormous activation tensors, gradients, optimizer states, and parameters. GPUs have prioritized high-bandwidth memory (HBM) and wide interfaces, plus increasingly capable interconnects for multi-GPU scaling.

The ability to stitch many GPUs together with fast links and mature collective communication libraries is a major advantage. AI training is often distributed, and scaling efficiency depends on low-latency, high-throughput communication. A chip that is “fast” in isolation can lose badly once you factor in multi-device training overhead and system-level bottlenecks.

GPUs win on availability and system integration

Even if a custom accelerator is faster on paper, you still need servers, racks, cooling, drivers, orchestration, monitoring, and a procurement pipeline that works. GPU ecosystems have battle-tested configurations across cloud and on-prem deployments, plus a large pool of engineers who know how to run them reliably.

This maturity reduces risk. When deadlines matter—research timelines, product launches, service-level targets—teams prefer a platform with predictable behavior and known failure modes.

Where custom accelerators and NPUs already shine

Specialized chips do win in certain settings:

• Inference at the edge: tight power budgets, predictable models, and fixed batch sizes can favor NPUs.
• High-volume inference in data centers: when the model is stable, kernels can be heavily optimized, and utilization is high.
• Quantized workloads: some accelerators have excellent INT8/INT4 throughput with low power.
• Cost-sensitive deployments: if a chip is cheap and good enough, it can be the best business choice.

So the question isn’t whether accelerators can beat GPUs. They already do in narrow lanes. The hard part is beating GPUs across enough workloads, with enough usability, to become the default.

What it would take to dethrone GPUs

1) A software stack that feels boringly reliable

To replace GPUs, an accelerator needs first-class support across major frameworks, stable compilers, strong kernel libraries, and tooling that engineers trust. It must handle model churn without weeks of hand-holding.

Compatibility matters too: operators, numerics, mixed precision behavior, and debugging need to match developer expectations. If engineers have to rewrite models or avoid common techniques, adoption slows.

2) Strong performance on end-to-end training, not just one operator

Many accelerators advertise impressive TOPS, but training success depends on the whole graph: data movement, memory pressure, kernel launch overheads, and weird ops. A challenger must show consistent wins on real training runs, including optimizer steps, checkpointing, and distributed scaling.

It also needs to cope with irregular workloads: variable sequence lengths, dynamic batching, routing in mixture-of-experts, and sparse patterns that don’t map neatly to fixed-function hardware.

3) Memory capacity and bandwidth that scale with model size

Winning inference is easier than winning training, because training amplifies memory needs. A GPU challenger must offer competitive HBM bandwidth and enough memory per device to reduce fragmentation and communication overhead.

If the accelerator requires excessive model sharding, complex partitioning, or frequent host-device transfers, real throughput will suffer.

4) A system story: interconnect, networking, and collectives

The future of training is multi-device. Any contender must provide fast device-to-device links, mature collective communication, and predictable scaling behavior. It needs to perform in large clusters, not just in a single box.

5) A clear economic advantage

To displace GPUs, accelerators must win on total cost of ownership: purchase price, power, cooling, utilization, and engineering overhead. A chip that is 20% faster but 2× harder to operate rarely wins. A chip that is 2× more efficient and easier to deploy might.

6) A stable roadmap and supply

Enterprises bet on platforms for years. A contender needs continuity: multiple generations, backward-compatible software, and predictable supply. Without that, teams hesitate to commit.

The likely outcome: coexistence, with pockets of dominance

GPUs are still king because they’re a complete package: flexible compute, strong memory, scalable systems, and a software ecosystem that reduces friction. Custom accelerators and NPUs will keep gaining ground where workloads are stable, power is constrained, or economics demand specialization. Dethroning GPUs outright would require not just a faster chip, but a platform that matches GPUs in programmability, tooling, scaling, and availability—while also delivering a compelling cost and efficiency edge.