Skip to content
Stork.AIstork.ai
AI Tool

Neural Amp Modeler Review

Free, open-source AI guitar amp modeling platform for hyper-realistic captures of analog gear and signal chains.

shipped Jul 6, 2026aifreemium
ai
Neural Amp Modeler — product screenshot

Why it matters

1An open-source and free platform for neural network-based audio emulation.
2Released Neural Amp Modeler Architecture 2 (A2) in early June 2026, improving accuracy and reducing CPU usage.
3Achieves over 99% accuracy in independent comparisons against source amplifiers.
4Integrated into hardware from companies including Valeton, Darkglass, NUX, Dimehead, Chaos Audio, and Blackstar.

About Neural Amp Modeler

Business Model
Open Source
Founded
2026
Platforms
Web
Target Audience
Audio engineers, musicians, developers

Leadership

Steven Atkinson
Open Source

Specs

API Docs

View Documentation →

API Available

Yes, public API

overview

What is Neural Amp Modeler?

Neural Amp Modeler is a deep learning tool developed by Steven Atkinson that enables guitarists, bassists, producers, and audio engineers to capture and emulate the sound of guitar amplifiers and pedals. It uses neural networks to create highly accurate digital models of analog music equipment and entire signal chains. NAM captures the "essence" and behavioral characteristics of physical gear, including distortion, saturation, EQ, gain structure, compression, and dynamic touch sensitivity. Unlike traditional amp simulators that rely on algorithmic circuit simulations, NAM learns from recordings of real equipment, reproducing subtle characteristics like pick attack and transient response. It integrates into Digital Audio Workstations (DAWs) as a plugin, allowing for reamping DI tracks and working alongside other effects like EQ and compression. NAM excels at capturing amp-only tones and is generally recommended to be paired with separate Impulse Responses (IRs) for speaker cabinet simulation for greater flexibility; it is not designed to capture time-based effects like delay or compression directly.

features

Key Features of Neural Amp Modeler

Neural Amp Modeler provides a robust set of features for advanced audio emulation, leveraging deep learning to deliver high-fidelity sound reproduction.

  • Open-source audio plugin compatible with various Digital Audio Workstations (DAWs).
  • Utilizes neural networks and deep learning technology for highly accurate audio emulation.
  • Captures and emulates guitar amplifiers, pedals, and entire signal chains with hyper-realistic detail.
  • Offers an API for developers, with documentation available at https://www.tone3000.com/developers.
  • Achieves high accuracy in modeling complex characteristics such as distortion, saturation, EQ, gain structure, compression, and dynamic touch sensitivity.
  • Supports Neural Amp Modeler Architecture 2 (A2), released in early June 2026, for improved accuracy and significantly lower CPU usage.
  • Enables compatibility with embedded hardware, allowing A2 Light models to run on low-cost hardware like a $3 ARM Cortex-M7 600MHz chip.
  • Facilitates community-driven model sharing through platforms such as Tonehunt.org, fostering a vast library of user-created captures.

use cases

Who Should Use Neural Amp Modeler?

Neural Amp Modeler targets a diverse audience of musicians and audio professionals seeking high-quality, flexible, and accessible amp modeling solutions.

  • Guitarists & Bassists: For capturing and recreating the sound of real amplifiers, pedals, and signal chains, or exploring a vast library of community-created models.
  • Producers & Audio Engineers: For studio recording with authentic, mix-ready guitar tone without physical hardware, and for reamping DI tracks within DAWs.
  • Home Recording Musicians: To achieve professional-quality amp modeling without expensive equipment, leveraging the free and open-source nature of the platform.
  • Touring Musicians: For live performance by loading tones onto compatible guitar or bass pedals, eliminating the need to transport heavy and delicate physical gear.
  • Musicians Archiving Vintage Equipment: To digitally preserve and archive unique hardware tones or rare vintage equipment for future use and study.

how to use

How to Use Neural Amp Modeler

Getting started with Neural Amp Modeler involves installing the plugin and loading models, with options for advanced users to capture their own gear.

  • 1Download and install the Neural Amp Modeler plugin (VST3/AU) into your Digital Audio Workstation (DAW).
  • 2Load existing NAM models from community libraries, such as those found on Tonehunt.org, directly into the plugin.
  • 3Pair NAM amp models with separate Impulse Responses (IRs) for speaker cabinet simulation to achieve a complete guitar tone.
  • 4Integrate the NAM plugin with other digital audio effects like EQ and compression within your DAW for mixing and sound shaping.
  • 5For advanced users, capture your own physical amplifier or pedal by following the NAM training process, often utilizing Python packages or Google Colab.
  • 6Load NAM models onto compatible hardware pedals from manufacturers like Valeton or Darkglass for live performance applications.

pricing

Neural Amp Modeler Pricing & Plans

Neural Amp Modeler operates on an open-source model, making its core software freely available. The platform includes a free tier, allowing users to access and utilize its neural network-based amp modeling capabilities without cost. While the core plugin is free, some hardware integrations or community-supported services may have associated costs.

  • Free Tier: Access to the open-source plugin and a vast library of community-created models, with no direct cost for the software itself.

Pros

  • +Free and open-source platform, making advanced amp modeling accessible to all users.
  • +Exceptional sonic realism and dynamic response, often matching source amps with over 99% accuracy in independent comparisons.
  • +Vibrant and active community for model sharing, support, and a vast library of free captures (e.g., Tonehunt.org).
  • +Neural Amp Modeler Architecture 2 (A2) offers improved accuracy and significantly lower CPU usage, enabling integration into low-cost embedded hardware.
  • +Ability to capture and archive personal physical gear, including rare or vintage equipment, for digital preservation.
  • +Eliminates the need for expensive hardware or studio sessions to achieve high-quality, mix-ready guitar tones.

Cons

  • −Higher technical barrier for capturing and training custom models compared to commercial, more streamlined software.
  • −Not designed to directly capture time-based effects like delay or compression, requiring separate plugins for these effects.
  • −Requires separate Impulse Responses (IRs) for speaker cabinet simulation, adding an additional step to the signal chain setup.
  • −Prior to the A2 update, standard architecture NAM captures could be CPU-intensive, posing challenges for large mixes.
  • −Relies heavily on community contributions for model variety, which can result in varying levels of quality and consistency.

Similar Tools

Neural Amp Modeler vs Competitors

Neural Amp Modeler distinguishes itself in the market through its open-source nature and community-driven development, offering a unique alternative to commercial amp modeling solutions.

1
IK Multimedia TONEX↗

TONEX utilizes proprietary AI Machine Modeling™ technology to create hyper-realistic "Tone Models" of amps, cabs, and pedals that are virtually indistinguishable from their physical counterparts.

Unlike Neural Amp Modeler's open-source and entirely free platform, TONEX offers a freemium model with a free CS version and paid tiers, providing a more integrated ecosystem that includes software, a mobile app, and dedicated hardware pedals. It allows users to capture their own gear and share or download models via its ToneNET community.

2
Neural DSP (Plugins)↗

Neural DSP leverages advanced neural network technology, referred to as "Neural Capture," to power its acclaimed plugins and hardware, delivering hyper-realistic and controllable full circuit models.

Neural DSP's plugins are premium, paid products, contrasting with Neural Amp Modeler's open-source and free nature. While both use neural networks for emulation, Neural DSP focuses on meticulously crafted, artist-signature plugins and a powerful hardware unit (Quad Cortex) that also incorporates Neural Capture technology.

3
GuitarML (Proteus Plugin)↗

GuitarML provides VST3/AU/Standalone plugins that use machine learning and neural networks to create and load digital models of amps and pedals, with a unique emphasis on user-created captures and a DIY hardware option called NeuralPi.

Similar to Neural Amp Modeler, GuitarML focuses on neural network-based gear capture and offers plugins for loading these models. While NAM is entirely open-source, GuitarML provides its own plugins and a platform for user-created models, and uniquely offers a DIY hardware project (NeuralPi) for running these models.

Visit Neural Amp Modeler↗

AI Reputation Report

Is Neural Amp Modeler yours?

ChatGPT, Perplexity, Gemini, Claude & Grok answer buyer questions about Neural Amp Modeler every day. See whether they name Neural Amp Modeler — or send buyers to a rival.

See what AI saysfree preview

Featured in articles