Neural Amp Modeler: The AI Revolution For Guitarists
Discover Neural Amp Modeler (NAM), the open-source AI technology redefining guitar tone with authentic, capture-based amp modeling.
The evolution of guitar tone has followed a familiar arc punctuated by a few major milestones—and a few setbacks.
First came the electric guitar and amplifiers powered by vacuum tubes, which gave way to the development of solid state amps. While solid state amps are more reliable and less delicate than tube amps, it’s hard to argue they produce better tone. The next milestone was the invention of digital modeling effects and amplifiers. They offered a massive amount of tonal options and convenience, but they definitely didn’t sound as good as the real thing. As the tech developed, manufacturers started making software emulations of nearly every piece of analog gear you could ever want. Sadly, the best of digital amp modelers are wildly expensive, and the worst of them sound terrible. Today, with the latest developments in AI and Machine Learning, we’ve crossed a new threshold: Neural Amp Modeler. It's the moment AI truly changes how we capture, share, and play guitar and bass sounds.
“The next major innovation in guitar technology is about letting AI learn the character of your rig so you can take that sound anywhere.” - Steven Atkinson, inventor of Neural Amp Modeler
Unlike traditional systems that approximate circuits with hand-tuned code, NAM uses modern machine learning (AI) to learn the behavior of amps, pedals, and gain stages from real recordings. The result is tonal accuracy and feel that many players now describe as indistinguishable from the original gear.
NAM captures guitar and bass tones with shocking accuracy (Image credit: Anthony Gordon)
The Birth of NAM: A Fun Idea That Caught Fire
NAM didn’t begin as a business venture, but as a fun experiment. Enter Steve Atkinson, a gifted AI and ML engineer who also happens to be a multi-instrumentalist and a devoted heavy metal guitarist. Steve says that, “I got into machine learning almost 10 years ago, but I’ve been a musician since I was a kid. NAM started as a ‘what if’ fun project for myself as a guitarist.” The question changed everything: what if you trained an AI model using actual guitar playing as a data set? Would it then be possible not only to capture tones, but also capture the accurate feel and response you got from playing a guitar through an amp in the real world?
As a musician, Atkinson had used and loved digital modelers like the Fractal Audio Axe-FX II for years, “it’s still near and dear to me,” he says. But as an AI engineer, he also knew that AI and ML offered training possibilities that never existed before. Steve explains, “These digital models were just looking at the circuit to try and represent those analog sounds. But I knew that when you go to a neural network you could do all sorts of things, specifically because it's nonlinear. You could give it so much more data beyond just modeling the circuit.”
He deliberately built the first version of NAM as a “closed book.” Instead of leaning on existing guitar-modeling research, he wanted to see how far his intuition about the deep-learning process could go. The early signs came in 2019 when some NAM profiles he processed offline started sounding suspiciously close to the real amp. Then came the “a-ha” moment—playing live through the first plugin build. Steve describes the moment; “Hearing it jump out of the speakers as I played—that was thrilling.” Soon after that, his neural amp models passed the audio version of the Turing Test. “At this point,” he says, “comparing tones from real amps with neural amp model profiles is indistinguishable to me.”
NAM blurs the lines between virtual and reality
A key decision from the start: keep NAM open source. That choice didn’t just invite community contributions; it changed the shape of the ecosystem—accelerating experimentation, enabling hardware builders to experiment with NAM, and giving artists confidence that their tones won’t be locked in a walled garden or behind a paywall.
One more benefit to the open-source model is that it’s building an ecosystem designed to last. “Being open source is a kind of insurance against obsolescence,” Atkinson says. “Software and hardware both need ongoing care, but with an open codebase, knowledge can be preserved and carried forward.”
Why AI Makes Neural Amp Modeling Possible Now
If this is all such a good idea, how come nobody developed it sooner? The answer is because they couldn’t. Recent advancements in AI learning, open ML tooling, and practical computing have made it all possible. Traditional modelers (think Line 6, Kemper, Fractal, etc.) pioneered digital tone modeling—and they’re brilliant engineering achievements. But they are largely hand-designed approximations or circuit-inspired models created by engineers at some manufacturer somewhere. NAM flips that: it’s data-driven. You feed it real recordings of your rig responding to musical inputs, and a neural network learns the behavior directly. “It isn’t a hand-coded approximation,” Atkinson explained. “It’s learning the sonic fingerprint of the gear.”
This approach is why many users find NAM as good as—or better than—legacy modelers for tonal authenticity and feel. The nuance you capture is the nuance you play back.
Who Needs NAM and Why
Any guitarist, bassist, audio engineer, mixer, or producer who loves great tone can benefit from the magic of NAM.
- Bedroom Guitarists & Home Studio MusiciansNAM is headphone-friendly, lets you record silently at 2 a.m., and get your amp’s tone without hauling heavy gear around or waking the neighbors. Access to over 100,000 free NAM profiles on TONE3000 also means you get to play dream gear you’ll never see in person.
- Producers & Recording EngineersNeural amp modeling offers recall perfection. Capture an artist’s killer sound once; reuse it for overdubs, edits, and post production without re-miking or re-amping or worrying about room variables. Or, swap out an artist’s less desirable tone with one you’ve selected from the massive free archive on TONE3000.
- Collectors & ArchivistsEven in a digital age, most musicians still worship at the altar of vintage gear. NAM allows you to capture the essence of rare or aging equipment, so you’ll always have those tones on tap for your next session. Not only that, if you have old recordings with tones you love and want to recreate, you can easily do that by feeding the matching DI and wet guitar tracks into TONE3000. The software will learn how the dry signal relates to the wet one and can create a convincing NAM profile just from those tracks.
NAM doesn’t replace vintage gear. It preserves it forever.
Tracking and Performing Live: Latency, Feel, and Hardware
NAM runs extremely efficiently in modern DAWs and on embedded hardware devices. Latency depends on your setup—audio interface, buffer size, and the power of your own CPU are all factors—but the tech itself is designed specifically not to strain your CPU. This level of efficiency is a feature, not a bug.
Speaking on the issue of latency, Neural Amp Modeler inventor Steve Atkinson says “on a typical laptop/interface I get a handful of milliseconds and I’m happy. But the best dedicated hardware NAM players are stunning—there’s only about 0.5 ms latency through one NAM pedal I’ve seen. That’s roughly 20× better performance than my laptop.”
Olli Paajanen of Darkglass Electronics breaks down how NAM works—and how you can use it live in a stompbox
The holy grail of all guitar modeling tech has always been about capturing that essence that exists just beyond the realm of tone, which is feel. The less taxing on your CPU a tone model is, the more natural feel you’ll get. Right now, nothing captures that level of feel more than NAM.
Open Source, Community Pace, and the “Walled Garden” Problem
Because NAM is open source, builders can integrate it into both hardware and software products, and the community can contribute to its evolution. This doesn’t just future-proof the technology, it ensures this tech will be the future. This stands in stark contrast to closed ecosystems where tones and purchases are locked to one platform—often with a premium price tag.
On the future of NAM, Atkinson favors innovation over gatekeeping. “The open, collaborative approach has already pushed a data-driven modeling style into more products. I hope more companies adopt it if it helps them make better instruments.”
Want to play NAM captures right now—without capturing your own?
If you’re curious just to hear how these NAM models sound, there’s no reason you have to record your own just yet (but we know you’ll want to). Here’s how to get started.
- Visit TONE3000
- Browse models of amps, pedals, and rigs—audition classic tones, or search for more obscure stuff you’ll likely never find in your local guitar store.
- Download a few favorites and load them in the free NAM plugin (or a compatible hardware player), fire up your DAW, and load those NAM profiles onto your guitar tracks.
- Hear it for yourself and prepare to be amazed. Seriously.
Why It’s a Big Deal—Beyond “Just Another Modeler”
So what are the main differences between Neural Amp Models and plain-old guitar modeling gear? A lot, as it turns out.
- Authenticity: Because NAM learns from your rig responding to musical input (either through the custom-designed Sweep Signal, or through your own playing), it nails the nuance of your equipment.
- Portability: It delivers your sound, minus the weight, noise, and setup time.
- Recall: Overdub guitar tones that feel identical—because the tone is identical.
- Community: Tens of thousands of players on TONE3000 already contribute NAM profiles, growing the sheer volume of available tones faster than any single company can build.
- Creativity: When you’re not using your own tones, take advantage of the TONE3000 community to audition NAM captures from players modeling gear you don’t have, but wish you did.
- Future-Proofing: Open source means the tech will continue to develop and be supported.
- Quality: The sound quality stands shoulder to shoulder with the best guitar modeling gear in the world. In many cases, players prefer it.
- It’s Free: There’s no software to purchase and you don’t need to buy anything else to make it work with most basic recording setups.
The AI Era of Guitar Tone Is Here
Companies like Line 6, Kemper, and Fractal proved a long time ago that digital modeling could approximate a good tone. Neural Amp Modeler—powered by modern AI and an open community—pushes beyond approximation into capture. NAM lets you capture that lightning in a bottle, and then unleash the lightning whenever you want it. For those of us searching for the perfect tone, being able to capture it, own it, and use it whenever we want is nothing short of a miracle.
Ready to hear it for yourself? Head to TONE3000, load a few models, and plug in. The future of guitar tone isn’t just coming—it’s already in your browser.
