What is local inference?
Most AI tools work the same way: you send a request to a server, the server runs the model, you receive the result. Local inference flips this. The model runs on your machine. No request leaves your network.
Definition
Local inference is the execution of an AI model on hardware you own or control, rather than on a remote cloud server accessed via API. The model weights are loaded directly onto your GPU, inference runs locally, and outputs are generated without any external network call.
This applies to the full generation pipeline: prompt encoding, the denoising or generation loop, and decoding the output. Everything happens on your hardware.
The difference from cloud API inference
Cloud API inference sends your inputs (text prompts, reference images, audio files) to a provider's server. The provider runs the model, returns the output, and charges per request or per second of generated content. You have no visibility into what happens to your data on the server.
Local inference keeps everything on your hardware. Your prompts, your reference assets, your generated outputs, and your fine-tuned model weights never leave your machine.
For studios, agencies, and enterprises working with proprietary IP, unreleased footage, or confidential brand assets, this is a meaningful structural difference. Not just a privacy preference. A hard requirement.
What local inference requires
Running a large video generation model locally requires sufficient GPU VRAM to load and run the model weights. An unquantized 20-billion-parameter model in BF16 requires roughly 40GB of VRAM. With quantization to INT8 or INT4, that drops to 10–20GB, within range of consumer GPUs like the RTX 4090 (24GB) or RTX 5090 (32GB).
Beyond VRAM, local inference requires the model weights (either downloaded or trained locally), a runtime environment (Diffusers, ComfyUI, PyTorch), and a compatible driver stack.
Zero marginal cost
The most significant economic consequence of local inference is zero marginal cost per generation. Once hardware is purchased, each additional generation costs nothing beyond electricity. For high-volume workflows (producing hundreds of shots per day, running batch generation for ad campaigns, iterating through many takes on a scene), this eliminates entire cost categories.
Cloud API pricing is per-second of generated content. At scale, those costs compound quickly. Local inference removes the compounding.
A brief history
Local inference for large AI models became practically accessible around 2023, driven by two developments: the release of open-weight models large enough to be competitive with closed alternatives, and advances in quantization that allowed those models to fit on consumer hardware.
The LLaMA ecosystem established the pattern for language models. For image generation, Stable Diffusion had already demonstrated local inference at scale on consumer hardware from 2022. Video generation models took longer due to their larger size and higher compute requirements.
LTX-2 and local inference
LTX Desktop runs LTX-2.3 100% locally on consumer-grade hardware, with no API calls, no cloud dependency, and no per-generation fees. The 1/5 to 1/10 compute cost of LTX-2.3 compared to earlier models is a key enabler: it brings the memory footprint within range of GPUs enthusiasts and studios already own.
For teams that need cloud flexibility, the LTX-2 API provides the same model via a managed endpoint with per-second pricing.
