Neural Amp Modeler
Key Features
- Deep learning-based amp profiling that captures the tonal response, dynamics, and feel of real guitar amplifiers, cabinets, and effects pedals
- Over 6,500 community-created profiles available on Tone3000, covering vintage cleans, crunch, and high-gain tones from amps like Fender, Marshall, Peavey 5150, and ENGL
- Built-in noise gate and impulse response (IR) loader for cabinet simulation directly within the plugin
- Profile your own gear using the open-source training tools via Google Colab, Tone3000 online, or local Anaconda setup
- Universal Binary with native Apple Silicon (M1/M2/M3) support on macOS, plus growing hardware ecosystem including Darkglass Anagram, Valeton GP series, and Hotone Ampero II
- Active open-source community with 25,000+ members in the official Facebook group and continuous development of new architectures including the upcoming A2 engine
Description
Neural Amp Modeler (NAM) is an open-source guitar amp and pedal profiler created by AI researcher and musician Steven Atkinson. It uses deep learning to capture the sonic characteristics of real amplifiers, cabinets, and effects pedals with what many users and reviewers describe as state-of-the-art accuracy.
Unlike traditional algorithmic amp sims that model individual circuit components, NAM works by training a neural network on audio captured from actual hardware. The result is a .nam profile file that reproduces the tonal response, dynamics, and feel of the original gear rather than an approximation of its schematic.
The plugin ships with a built-in noise gate and IR loader for cabinet simulation, and over 6,500 community-created profiles are available on Tone3000 (formerly ToneHunt) covering everything from vintage Fender cleans to high-gain Peavey 5150 and ENGL models.
Bedroom Producers Blog calls the sound quality "absolutely killer" and notes that NAM delivers response and feel often lacking in conventional amp sims. Reddit users on r/GuitarAmps frequently compare it favorably to the Kemper and IK Multimedia TONEX, with several reporting it replaced their physical amp rigs entirely.
NAM runs as a VST3 and AU plugin on Windows 10+ (64-bit) and macOS 10.15+ with Apple Silicon support via Universal Binary. A standalone application is also included, and CLAP, AAX, and Linux LV2 support are in development.
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Frequently Asked Questions
Do I need a DAW to use Neural Amp Modeler?
No. Neural Amp Modeler includes a standalone application that works without a DAW, so you can plug your guitar into an audio interface and play directly through NAM. However, the standalone mode only supports input 1, so if your interface has multiple inputs you may need to use a DAW for routing flexibility.
What is neural amp modeling and how does it differ from traditional amp sims?
Traditional amp simulators use algorithmic models that replicate individual circuit components mathematically. Neural amp modeling instead trains a neural network on audio captured from real hardware, producing a profile that reproduces the actual tonal response and dynamics of that specific amp. This approach often sounds more realistic because it captures the full nonlinear behavior of the hardware rather than approximating it.
How do I create my own amp profiles with NAM?
You need an audio interface, a DI box, and the amp or pedal you want to capture. Record a standardized reamp signal through your gear, then upload the dry and wet audio files to Tone3000 online, Google Colab, or run the training locally with Anaconda. The training process takes a few minutes and outputs a .nam file you can load into the plugin.
Is Neural Amp Modeler compatible with TONEX or Kemper profiles?
No. NAM uses its own .nam profile format and is not directly compatible with TONEX Tone Models or Kemper profiles. Each platform uses a different training architecture. However, you can capture the same physical amp in NAM format separately, and some community members have profiled amps that overlap with popular Kemper and TONEX captures.
