Project
NAM AUv3
Real amp tone on iOS and Mac — load any NAM model and play
What it is
NAM AUv3 is an iOS and Mac AUv3 plugin that loads Neural Amp Modeler (.nam) profiles and cabinet impulse responses (.wav) for real-time guitar amp simulation. Built on Swift 6 with a C++ DSP kernel and the vendored NAM Core (using Eigen and ConvNet) for neural inference on-device.
Who it’s for
Guitarists and producers using iOS and Mac DAWs like AUM, GarageBand, Cubasis, Logic Pro, or Loopy Pro who want real tube amp tone without external hardware.
Features
Neural amp modeling
Load any community-trained .nam model and play through a neural capture of a real amp, in real time.
Cabinet IR loading
Drop in any .wav impulse response to shape the cabinet, room, and microphone character.
3-band EQ
Shape your tone with a built-in low, mid, and high EQ stage after the model.
Noise gate
Tame idle hum and pickup noise with a configurable threshold gate before the model.
Normalized / raw / calibrated output
Match level to your host or preserve the true modeled output with three output modes.
Works in any AUv3 host
Runs as an Audio Unit extension on iOS and Mac — Logic Pro, AUM, GarageBand, Cubasis, Loopy Pro, and other compatible hosts.
Tech
- Swift 6
- C++ DSP kernel
- Vendored NAM Core (Eigen, ConvNet)
- Real-time audio
- AUv3 extension architecture
- iOS 17+ / macOS 15+
Links
A plugin, not another amp modeler
NAM AUv3 focuses on being a great host plugin rather than reimplementing the modeling. The neural inference is handled by the same vendored NAM Core used by the desktop Neural Amp Modeler project, so any model that loads in the desktop tool loads here too — thousands of free community captures are available at TONE3000 and ToneHunt.
Workflow
- Open NAM AUv3 inside your host on iOS and Mac (AUM, GarageBand, Cubasis, Logic Pro, Loopy Pro).
- Pick a
.nammodel file — a neural capture of a real amp. - Optionally load a cabinet impulse response (
.wav) to shape the cabinet. - Dial in the noise gate, EQ, and output mode.
- Play. The DSP runs in the audio thread with the host's real-time priority.
Why neural amp modeling
Neural amp modeling captures a real amplifier's response across its full operating range — clean, edge of breakup, and high-gain — rather than approximating it with a handful of EQ curves. A well-captured model reproduces how the amp reacts to your guitar's volume knob, your pick attack, and the pedals in front of it, in a way traditional amp sims struggle to match.
The model ecosystem
Because NAM AUv3 reads the standard .nam format, you get access to a growing library of free community models:
- TONE3000 — curated, downloadable amp captures and IRs.
- ToneHunt — a large community archive of models and impulse responses, free to download and use.
The same files you'd use on desktop work on iOS and Mac — no conversion, no retraining.
Built on NAM Core
The plugin ships the upstream NAM Core C++ inference engine (built on Eigen and the ConvNet implementation) inside the AUv3 extension. That keeps model loading and DSP behavior consistent with the desktop Neural Amp Modeler project while running entirely on-device.