What is the difference between GPU and CPU in voice AI?
A CPU is a general-purpose processor optimized for sequential, low-latency tasks; a GPU is a specialized processor optimized for massively parallel computation, originally designed for graphics and now widely used for deep learning. In voice AI, the choice between them shapes where and how a model can run.
How GPU and CPU compare in voice AI?
GPUs offer very high throughput for batched inference but come with cost, scheduling overhead, and infrastructure complexity. CPUs are ubiquitous, cheap, and low-latency per request, but historically not powerful enough for real-time neural audio processing at scale. Closing that gap requires careful model design and a highly optimized inference engine.
How does ai-coustics use CPU vs GPU?
Quail is designed to run on CPU only. Combined with our AirTen inference engine, that means voice enhancement, voice focus, and VAD can run alongside the rest of the voice pipeline on the same hardware, no GPUs required. Teams can run concurrent sessions easily, which keeps latency low and infrastructure simple.
