CUDA or WebGPU?

The move toward GPU computing is massive, and not only driven by AI.
The parallel processing capabilities of modern GPUs exceed those of general-purpose CPUs by orders of magnitude.

CUDA has become the dominant tool to harness this power. Its performance and flexibility are remarkable, and for many high-end applications, it remains the natural choice.

So why even consider WebGPU?

Beyond raw performance

At Senslogic, we believe computing is not only about maximizing performance. It is also about how knowledge is distributed and built within an organization.

High-performance tools are valuable, but only if they are accessible, shareable, and understandable across teams.

WebGPU as an enabling layer

WebGPU brings GPU computing into the browser:

• No installation
• No platform dependency
• A single environment for computation, visualization, and user interaction

The browser becomes a unified runtime. Everyone accesses the same version, the same tools, and the same models—instantly.

A different kind of advantage

CUDA offers unmatched control and peak performance.
WebGPU, while approaching similar computational capabilities, offers something different:

• frictionless deployment
• platform independence
• immediate accessibility

This makes it particularly powerful for building in-house competence, where tools are not confined to specialists but can be shared across an organization.

Conclusion

This is not a question of CUDA versus WebGPU.

It is a question of priorities:

• maximum performance
• or accessible, distributed computation

In many cases, the real advantage lies not in more compute power—but in making that power available to more people.