GPU NAM 1.0.0

GPU NAM 1.0.0 — neural amp captures on the GPU

Load a Neural Amp Modeler .nam capture and run its WaveNet inference to reamp your signal. CPU by default, with an opt-in GPU engine running the exact same model.

Honest about the GPU

Naive per-sample GPU inference is terrible (round-trip per sample). Done right — block-parallel (the network is feedforward, so a whole block computes in parallel) and fused into one GPU submit per block — it wins, and the win grows with model size. The GPU output is validated bit-for-bit against the CPU reference (cross-correlation 1.0). A standard capture runs real-time on either engine (GPU with headroom); a large capture (48-channel, two 16-layer stacks) runs ~9.5× faster on the GPU — the regime where the CPU can't keep up. The status line shows the live per-block cost and how much of real-time it's using.

A small example model is bundled so it runs out of the box; load your own .nam captures for real amp tones.

One notarized installer (Customize → AU / VST3 / CLAP / Standalone). Apple Silicon; run your host natively (not Rosetta).

Plays the open MIT Neural Amp Modeler format; inference is an independent implementation. Bundled example model from sdatkinson/NeuralAmpModelerCore (MIT), with attribution.