Real-time noise removal is a small neural network running in a loop. Many times each second it receives a sliver of microphone audio, estimates which parts are voice, and passes along a cleaned version before the next sliver arrives. That work has to happen somewhere, and products have settled into two camps: those that run the model on the computer’s main processor and those that hand it to the graphics card. Which camp suits you depends less on which is “better” than on what hardware you own and what else that hardware is doing while you talk.
How the CPU approach works
The central processor is the one component every computer is guaranteed to have, so software that targets it can run almost anywhere: thin office laptops, older desktops, Macs and Windows machines alike. Krisp is the best-known example of this design. It installs as a virtual microphone and speaker, does its processing locally on the processor, and works with whichever calling app you point at it.
The constraint is the budget. A CPU is a generalist that is already busy with the operating system, the meeting app, the browser and the video encoder. A model designed for it has to be compact and efficient enough to finish each audio block on time without hogging a core. Engineers have become very good at this, and well-optimized CPU models are light enough that most users never notice them. On an older or heavily loaded machine, though, the cost can show up as warmer temperatures, louder fans, shorter battery life, or, at worst, crackling audio when the processor cannot keep up.
How the GPU approach works
Graphics cards are built to do enormous numbers of simple calculations in parallel, which happens to be exactly what neural networks need. Recent NVIDIA cards also include dedicated hardware for AI workloads. NVIDIA Broadcast takes advantage of this: it runs its noise removal, along with room echo removal and camera effects, on the GPU. That is why it requires an RTX-class card and why it is a Windows product.
Moving the work off the CPU has a clear benefit for one kind of user. A streamer’s processor is often the bottleneck, juggling the game logic, the streaming software and chat tools. Shifting audio cleanup to the graphics card leaves that processor alone, and the GPU has the headroom to run a larger model than a CPU-bound product might risk.
The trade-offs are the mirror image. The hardware requirement excludes most business laptops and every Mac. A discrete GPU draws more power than an efficient CPU routine, which matters on battery. And during a demanding game the graphics card is not idle either; the audio effects take a small share of its capacity, which players chasing every last frame will want to measure on their own system.
Does the hardware change how it sounds?
Not by itself. Audio quality comes from the model, the data it was trained on, and how aggressively it is tuned, not from the chip that executes it. A GPU permits a heavier model, but a heavier model is not automatically a more natural-sounding one. Differences between products show up in the details: how they treat sudden noises like keyboard clatter, whether they preserve quiet speech, how they handle a second voice in the room, and how much delay they add.
Those are empirical questions, and they are best settled by testing both on the same recordings. A measured Krisp vs NVIDIA Broadcast comparison is more informative here than any argument from architecture, because it shows what each approach delivers on identical noise rather than what it could deliver in theory.
Matching the approach to the person
Think about where your machine has slack. A few typical cases make the choice clearer:
- A remote worker on a company laptop with integrated graphics has only one option, the CPU route, and should start with the suppression built into their meeting app before adding anything.
- A streamer or gamer with an RTX card and a busy processor is the person GPU processing was designed for.
- A Mac user, or someone who moves between several computers, needs a CPU-based tool for the simple reason that it goes wherever they go.
- A creator with a powerful desktop can use either and should decide by listening.
Try before you commit to either camp
Both approaches are mature, and either can produce clean, natural speech when it is set up sensibly. Whichever you lean toward, run it for a few days of normal use with a system monitor open. Watch processor and GPU load during your heaviest task, record a sample of your processed voice, and ask a regular contact whether anything changed. The right choice is the one your listeners do not notice and your computer barely feels.