Yes—you can generate text, images, and video on a Mac without paying a fee for each run, if you use local software and model weights you’re allowed to run. But “free” does not mean costless: your Mac, memory, storage, setup time, electricity, and waiting time still count. Video can take several minutes for a short clip, but available Mac measurements do not support “two hours per second” as a typical rate.
What “free” means for local AI on a Mac
Local generation avoids a per-generation service charge when the model runs on your Mac rather than through a paid API. If you already own a compatible Mac, you may treat its purchase price as a sunk cost when comparing the next generation with an API fee. That does not erase the computer’s original cost, electricity use, time spent installing software and downloading model weights, or the opportunity cost of waiting.
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There are also three separate permissions and cost questions: whether the app is free, whether the model weights may be used for your intended purpose, and whether you are using local inference or a paid API. For example, Lightricks describes LTX Desktop as free and open source and says local inference has no per-generation charge, while identifying API use as a separate paid option and model weights as subject to their own license terms. Check the current LTX Desktop documentation for requirements and terms; they can change.
How fast is local AI on a Mac?
There is no useful single “Mac speed.” Text generation, still-image diffusion, and video generation have different workloads. A speed figure only means something alongside the Mac and memory configuration, model, quantization or pipeline, output size, and measurement date. For text, prompt processing and the time to the first token are distinct from the rate at which later tokens arrive. For images and video, end-to-end generation time is the more practical measure.
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Text: memory and model configuration shape the result
Apple presents MLX as an open-source framework for Apple Silicon, with examples for local inference, fine-tuning, and generation. Its WWDC25 session says the MLX software is released under the MIT license and identifies LM Studio as an application using MLX for local text generation. The framework’s scope and examples are described in Apple’s MLX developer session and Apple’s MLX research page.
Quantization reduces a model’s memory footprint and can increase generation speed, but results depend on the particular model and setup. In an Apple-reported comparison, a 24 GB M5 MacBook Pro generated subsequent tokens 19–27% faster than a similarly configured M4 MacBook Pro across the tested MLX language-model architectures. The test used a 4,096-token prompt and measured 128 generated tokens; it is not a universal estimate for every model or real-world prompt. Apple reported memory bandwidth of 153 GB/s for the tested M5 configuration and 120 GB/s for M4. See the benchmark details from Apple.
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Memory sets a practical limit on which models fit and how smoothly they run. Apple’s M5 test reported inference footprints under 18 GB for an 8B BF16 model and a 30B MoE 4-bit model on a 24 GB MacBook Pro. At the other extreme, an Apple developer demonstrated a 670B-parameter model on an M3 Ultra Mac with 512 GB of unified memory; Apple said its 4.5-bit weights alone required around 380 GB. That demonstration shows what unusually high memory capacity can enable, not what ordinary local text generation requires. Apple’s WWDC25 developer material describes the demonstration.
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Apple and Hugging Face measured SDXL generation at 1,024 × 1,024 pixels and 20 inference steps in a benchmark using beta-era software in July 2023. The reported median latency across three back-to-back executions was 20 seconds on a Mac Studio with M2 Ultra. Other tested systems were slower: a MacBook Pro with M1 Max took 46 seconds, a MacBook Pro with M2 Max took 37 seconds, and a Mac Studio with M1 Ultra took 25 seconds. These are dated results for that model and configuration, not a guarantee for today’s models or every Mac. The benchmark and its conditions are documented by Apple and Hugging Face.
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Apple also reports that its M5 MLX setup generated a 1,024 × 1,024 FLUX-dev 4-bit image more than 3.8 times faster than its M4 comparison. This is a separate Apple-reported test; it should not be treated as a direct comparison with the 2023 SDXL results. Apple’s MLX page provides the benchmark context.
Video: the measured examples are far short of two hours per output second
One community project’s measurements for LTX-2.5 on a MacBook Pro M5 Pro with 64 GB used official model files, a distilled pipeline, and int8 quantization on load. The maintainer reported these results:
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| Output | Wall time | Peak MLX memory |
|---|---|---|
| 49 frames at 704 × 448 (2 seconds) | About 40 seconds | 20.9 GiB |
| 121 frames at 1,280 × 704 (5 seconds) | 207.5 seconds | 75.9 GiB; swapping reported |
In those runs, the compute time was about 20 seconds per output second for the two-second clip and 41.5 seconds per output second for the five-second clip. The larger run took longer per output second and reported swapping, illustrating how resolution, frame count, and memory pressure can change the result. These are project-maintainer measurements, not an independent lab comparison or a universal performance forecast. The setup, measurements, and model-file information are in the ltx-2-studio project documentation.
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What you need to budget beyond the per-run fee
Hardware fit and memory
- Check the model’s memory needs against the Mac’s unified memory and what is actually free when generation starts. A workload that fits on paper may still struggle if it swaps memory or other apps are using the system.
- Record the chip, memory capacity, model, precision or quantization, and any reported swapping when judging a speed result.
- Do not infer that every large model needs an unusually high-memory Mac from Apple’s 670B demonstration; it is an extreme-capacity example.
Storage and setup time
Model weights take disk space, and downloads add setup time before the first run. The community LTX-2.5 distilled setup lists about 71 GB for official model files, with optional additional files for other capabilities. That is an example specific to that project, not a general storage requirement for local AI. An external drive may help if internal storage is tight, but it is optional and must suit the software’s file-access needs.
Privacy and connectivity
When inference stays local, prompts and files can remain on the device. Lightricks says LTX Desktop works offline after its model weights have been downloaded. This benefit depends on using the local mode: an API mode sends the workload to a service, and its privacy and pricing terms are different. Apple also describes local Stable Diffusion as useful for privacy and offline use after the initial model download in its Core ML discussion.
How to decide whether local generation is worth it
Compare the specific workflow you plan to use, not a generic local-versus-cloud label. For a fair decision, write down:
- Cash cost: whether inference is local or billed through an API, and whether you already own the Mac.
- Hardware fit: chip, unified-memory capacity, free memory at launch, and whether the run swaps or fails.
- Task and quality target: text, image, or video; model and quantization; dimensions, frames, steps, and pipeline as applicable.
- Time: for text, separate prompt processing or first-token delay from subsequent token speed; for image and video, count the full run.
- Setup and storage: software installation, weight downloads, disk space, and any required supporting tools.
- Data handling: whether generation is genuinely local and whether the workflow needs a network connection after initial downloads.
The answer will vary by task: local text or image generation may be practical on hardware you already have, while large or high-resolution video runs can make waiting time and memory pressure the dominant costs. The available published measurements do not provide a current, directly comparable speed test across text, images, and video on one Mac, so compare like with like rather than treating any one figure as a universal Mac benchmark.
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