Hardware Requirements

Both LTX-2.3 and LTX-2.5 are large audio-video models, so local generation is substantially more demanding than conventional image-generation workloads. A 24 GB GPU provides a useful starting point for local workflows using quantized or memory-optimized configurations, while 32 GB or more gives additional headroom for higher resolutions, longer clips, and less aggressive offloading. The official LTX materials do not define a single VRAM number that guarantees every local inference configuration will fit. :contentReference[oaicite:0]{index=0}

A 24 GB NVIDIA GPU is a sensible baseline for users who want to run LTX models locally rather than through an API. GPUs in this class include cards such as the RTX 3090 and RTX 4090. With quantization, CPU offloading, and appropriate generation settings, 24 GB systems can be used for practical local video generation, although available resolution, clip length, and generation speed depend on the specific pipeline.

The important distinction is between 'can run' and 'runs comfortably'. Fitting the transformer into VRAM is only part of the memory budget: the text encoder, VAE, audio components, latent tensors, and intermediate activations also consume memory. Lower-VRAM configurations therefore rely on techniques such as quantization and CPU offloading rather than simply loading the complete pipeline in full precision.

The official LTX trainer documents an 80 GB GPU as the recommended configuration and provides a dedicated low-VRAM configuration for 32 GB GPUs. The 32 GB setup uses INT8 quantization, gradient checkpointing, an 8-bit optimizer, batch size 1, and an 8-bit text encoder to reduce memory consumption. The trainer documentation explicitly treats GPUs below 32 GB as unsupported for the shipped training configurations. :contentReference[oaicite:1]{index=1}

The same general hardware principles apply to LTX-2.3 and LTX-2.5. LTX-2.5 uses a large transformer together with a Gemma 4 12B text encoder and separate video and audio VAE components. Its official model repository provides split components for local execution, rather than treating the model as a single small checkpoint. :contentReference[oaicite:2]{index=2}

For users building a local LTX machine today, 24 GB should therefore be viewed as the practical consumer inference target, 32 GB as a stronger all-round configuration and the minimum documented tier for training, and 48–80 GB as the more comfortable range for demanding workloads and standard training configurations.

System RAM becomes especially important when using CPU offloading or quantized pipelines. As a practical target, 32 GB of system RAM is recommended for a 24 GB GPU, while 64 GB provides more headroom for large models, offloading, dataset processing, and training. The exact requirement depends on how much of the pipeline is kept in GPU memory.

The LTX trainer also materializes large model components in host memory during some quantization operations. For example, its current configuration documentation notes that loading the LTX-2.5 Gemma text encoder in 8-bit can still require roughly 26 GB of host RAM during materialization. :contentReference[oaicite:3]{index=3}

Local LTX installations also require substantial disk space. LTX-2.3 and LTX-2.5 distribute large model components alongside text encoders, VAEs, optional quantized variants, LoRAs, caches, and generated videos. A fast NVMe SSD with at least 100 GB of free space is a sensible starting point for a single local setup; users maintaining multiple model variants should reserve considerably more.

VRAM usage is not determined by model size alone. The main variables are model precision, quantization level, video resolution, number of frames, batch size, text-encoder precision, VAE processing, and whether components are offloaded to system RAM. Increasing resolution or clip length can substantially increase activation and latent memory even when the model weights remain unchanged.

For training, memory usage is additionally affected by optimizer state, gradients, activation storage, LoRA rank, gradient checkpointing, and validation sampling. The official trainer specifically recommends gradient checkpointing and reduced-precision components for constrained 32 GB configurations. :contentReference[oaicite:4]{index=4}

24 GB VRAM / 32 GB RAM: Practical entry point for local inference with memory-optimized or quantized workflows. 32 GB VRAM / 64 GB RAM: Better choice for regular local generation and the documented low-VRAM training configuration. 48 GB VRAM / 64–128 GB RAM: More comfortable for demanding inference and larger workloads. 80 GB+ VRAM: Recommended class for the standard LTX trainer configuration and serious training workloads. These tiers describe practical configurations rather than universal hard limits. :contentReference[oaicite:5]{index=5}

For the official LTX training stack, NVIDIA CUDA is the primary documented route. The trainer requires Triton, which is Linux-only, and the current documentation recommends CUDA 13+. Local inference has a broader range of community implementations, but compatibility depends on the specific application, backend, quantization format, and GPU. :contentReference[oaicite:6]{index=6}

Local inference: 24 GB VRAM is a practical consumer target; 32 GB+ is preferable for additional headroom. Training / LoRA: 32 GB VRAM is the documented minimum for the current low-VRAM trainer configuration. Standard training: 80 GB VRAM is recommended. System RAM: 32 GB is a practical inference target; 64 GB+ is preferable for heavy offloading and training. Storage: 100 GB+ free SSD space is a sensible starting point for a local installation with room for model components and outputs.