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NobodyWho and Cactus are both on-device inference engines, but they are built around different model ecosystems. NobodyWho runs GGUF models through llama.cpp, so it suits a team already working with that format. Cactus runs its own Cactus Quants (CQ) bundles, so it suits a project where the exact model you need is available as a prepared CQ bundle. Neither engine is established as faster in general. Choose on model availability, target devices, bindings, cloud policy and license, then benchmark on your own hardware.
Engine design: a llama.cpp layer versus a standalone stack
NobodyWho describes itself as a convenient local inference engine powered by llama.cpp. Its project documentation puts it this way: “All of this is enabled by Llama.cpp, while having nice, simple API.” In practice, llama.cpp handles model loading and inference, and NobodyWho adds a higher-level API on top.
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That higher-level layer includes streaming chat, tool calling, structured output, embeddings, speech-to-text, text-to-speech and retrieval-augmented generation. A September 16, 2026 side-by-side comparison article adds that tool-call grammars can be generated from function signatures.
Cactus is built as a layered stack: a high-level C inference engine, a zero-copy computation graph, hardware-specific kernels, and the Cactus Quants format. Its repository lists text, speech, vision, tools, embeddings, retrieval and cloud handoff as engine functions. Owning the full stack has a practical consequence: compatibility fixes and new model support follow Cactus’s own release cycle rather than llama.cpp’s. That is an inference from the architecture, not a documented guarantee, so read the release notes of whichever engine you adopt.
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Model format: GGUF through NobodyWho, CQ bundles in Cactus
GGUF with NobodyWho
NobodyWho accepts GGUF models through llama.cpp. If the model you need is already published as GGUF at the quantization you want, the question is whether your target build loads that file, not whether someone must convert it first. The September 2026 comparison article contrasts Cactus’s in-house model catalog with the much broader GGUF ecosystem, which is the main reason GGUF is the easier starting point for most teams.
CQ bundles with Cactus
Cactus’s current engine API reference describes a downloadable bundle as CQ weights plus a serialized graph and a manifest. That bundle is the unit to check. The same reference says conversion can quantize other Hugging Face models, but local runtime bundle generation for models outside Cactus’s hosted set is currently unavailable while its graph builder is being rewritten (as of October 2026). Unless your model is in the hosted set, do not expect to build a runnable Cactus bundle yourself.
Keep two questions apart: “does the converter accept this model?” and “does a supported, ready-to-run bundle exist for this model?” Only the second one matters for shipping. The sources checked also do not document loading a GGUF file directly into Cactus, so a GGUF-only model should not be planned as a Cactus deployment today.
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What the CQ quantization claims mean
The September 16, 2026 comparison article describes CQ as rotation-and-codebook quantization spanning 1 to 4 bits. That describes format capability. It is a vendor-side description, not a measure of output quality, on-device size or speed. Independent, matched quality measurements of CQ against GGUF quantizations are not available to cite, so judge any CQ model by running your own task on the exact bundle you will ship.
- The exact model name and version has a prepared CQ bundle for the Cactus release you plan to use.
- The bundle includes weights, graph and manifest, as the API reference describes.
- For NobodyWho, the GGUF quantization you ship loads in the build for each target platform.
- Output quality is tested on your prompts at the quantization you will actually deploy.
Hardware and acceleration
NobodyWho’s project README lists Vulkan and Metal GPU acceleration. Cactus’s repository documents ARM NEON SIMD kernels, with CPU and Metal execution options. These are different routes. NobodyWho’s advertised GPU path is Vulkan or Metal, while Cactus’s documented foundation is ARM NEON on the CPU, with Metal available as a backend choice.
Neither set of documentation shows that the accelerator path is used on every chip the engine supports. Check the specific device classes you target rather than assuming a GPU or NEON path applies everywhere.
Which engine is faster?
No independent benchmark compares these two engines on speed, and their documentation does not support a ranking. Speed depends on device, model, quantization, prompt length, generation settings and release. The two engines also use different quantization formats, so a fair test must control for that difference instead of comparing headline numbers. A reasonable protocol looks like this:
- Record each engine’s exact format and quantization. A GGUF file and a CQ bundle are not interchangeable, so label each result with its format.
- Fix the prompt set, context length, maximum output tokens and sampling settings, and use identical values for both engines.
- Run on the actual device model and OS version you will ship, with the same power source and a comparable thermal state.
- Measure time to first token, sustained generation speed and peak memory.
- Repeat the runs and report the spread, because sustained load on phones often triggers throttling and later runs can differ from the first.
Platforms and bindings
| Item | NobodyWho | Cactus |
|---|---|---|
| Listed bindings | Kotlin, Swift, Python, Flutter, React Native, Godot | Swift, Kotlin, Flutter, React Native, Python, Rust |
| Desktop | Linux, macOS, Windows (project README) | Not stated for desktop operating systems; Raspberry Pi and ARM Linux are named in the September 16, 2026 comparison article |
| Android | Kotlin, Godot, Flutter, React Native (project README) | Not stated per binding; phones are a stated target |
| iOS | Swift, Flutter, React Native (project README) | Not stated per binding; phones are a stated target |
| Embedded and ARM Linux | Not stated as an emphasis | Smart-home and robotic/embedded uses are documented; Raspberry Pi and ARM Linux are named in the comparison article |
| Browser (WebAssembly) | An open WASM issue is noted in the comparison article; no generally available target is established | No browser target is established |
Mobile and desktop coverage
A binding name is not proof of coverage. NobodyWho’s README ties mobile support to the binding, so a Flutter or React Native project gets both Android and iOS paths from the list above, while Swift is listed for iOS only. Godot appears only in NobodyWho’s binding list. Cactus’s repository positions the engine for phones and wearables, but the per-binding operating-system matrix is not stated in the material checked. Confirm the binding for each OS and architecture in the release you intend to ship.
Embedded and ARM Linux targets
Cactus reaches beyond phones into smart-home and robotic or embedded uses, and the comparison article names Raspberry Pi and ARM Linux as areas where it extends past NobodyWho’s stated deployment emphasis. Board, OS and architecture support changes between releases, so verify the exact board and OS image you plan to deploy.
Browser targets
Neither engine has an established, generally available browser (WebAssembly) target. The comparison article reports an open NobodyWho WASM issue and no Cactus browser target. Treat that as the status at the time of writing, not a permanent product limit, and do not plan a browser deployment on either engine until the target is documented in a current release.
Cloud behavior and offline operation
NobodyWho’s documentation presents the engine as offline local inference that needs no servers or API keys. Cactus also supports local inference, but it documents an optional handoff that can route difficult or low-confidence requests to a cloud model. Its repository describes the project as “A hybrid edge-cloud AI engine for mobile devices & wearables,” so cloud handoff is part of the design rather than an add-on you may never notice.
Turning off Cactus’s cloud handoff
The Cactus CLI exposes a --no-cloud-handoff option. Setting it is necessary but not sufficient on its own. Confirm that the same setting applies in the binding your app uses, and that the built app keeps it disabled.
Verifying that data stays on the device
“Local inference” does not by itself guarantee that an application never sends data off the device. Check the shipped build, not only the configuration file:
- Disable cloud handoff in configuration and confirm it stays disabled in the built app.
- Run the app with outbound network blocked, then exercise every feature that calls the model, including tool use and retrieval.
- Inspect any telemetry or analytics code in your app and in every SDK you bundle.
- Repeat the checks after each engine upgrade.
Vendor services
NobodyWho’s company site advertises onboarding, model selection, monitoring and support for on-device and on-premises deployments. These are commercial services to evaluate on their own terms; they are separate from the engine itself.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Licensing
NobodyWho: EUPL-1.2
NobodyWho’s repository identifies the project as licensed under EUPL-1.2 and states that it may be used in proprietary and commercial projects. It also says that distributed modifications to the repository must be open-sourced. Those are the obligations the project itself describes. Whether your application, which merely links the library, takes on any further obligation is a legal question, and the repository’s statement about modifications should not be stretched to cover it without review.
Cactus: source-available, with threshold-based commercial terms
A September 16, 2026 comparison article describes Cactus as source-available rather than OSI open source. It reports free-use thresholds based on funding and annual revenue, with a separate commercial license required above them, and says the terms were checked in September 2026. Cactus’s repository links to a LICENSE file, but the thresholds have not been confirmed against that file’s text here. Do not treat thresholds or deadlines as verified until you have read the license yourself.
Checks before you ship
- Read NobodyWho’s EUPL-1.2 license text and the repository’s statement on modifications.
- Read the LICENSE file in Cactus’s current repository and record the funding and revenue thresholds exactly as written.
- Decide whether you will ship modified copies of either engine, because that changes the obligations.
- Have counsel confirm the position for your company and jurisdiction.
Decision sequence
Work through these situations in order. The first one that fails usually decides the question.
Quick Recap
| Your situation | Start with | Verify before committing |
|---|---|---|
| The model is published as GGUF and your team already uses llama.cpp models | NobodyWho | The GGUF file loads in each target build, and the quantization performs on your own tasks |
| You need a Godot integration | NobodyWho | The Godot binding supports your target OS (Android is listed in the README) |
| The model exists only as GGUF and you want Cactus | NobodyWho, unless a prepared CQ bundle exists for the exact model | Confirm the bundle in Cactus’s hosted set before any Cactus planning |
| The target is a phone or wearable and the exact model has a prepared CQ bundle | Cactus | The bundle works in your Cactus release, the binding supports your OS, cloud handoff is disabled, and the license thresholds are confirmed |
| You need a Rust binding | Cactus | Rust support for your operating system and architecture |
| Your policy forbids any outbound network traffic | Either engine, once configured | NobodyWho: test network behavior in the shipped build. Cactus: disable cloud handoff and test the same way |
| The target is a browser | Neither engine is established | Treat browser deployment as unproven until a current release documents it |
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