Running this model locally is fastest when deployed through Docker.

Follow the sequence of steps detailed below.

The loader auto-caches the model archive (several GBs included).

To guarantee smooth performance, the installation process auto-selects the best possible options for your PC.

🗂 Hash: ceb56a02b3c9377ae482b6a42b630943 • Last Updated: 2026-06-24 Verify Processor: 6-core 3.5 GHz minimum required RAM: 64 GB to avoid OOM crashes on large contexts Disk Space: required: fast PCIe 4.0 drive for instant boots GPU: high memory bandwidth GPU for next-gen local AI pipeline

LTX-2.3-fp8 is a state‑of‑the‑art language model optimized for low‑precision inference. It features a parameter count of 7 B weights and achieves high throughput on consumer‑grade GPUs. The model leverages FP8 quantization to reduce memory footprint while preserving nearly full‑precision performance. Its architecture incorporates a refined attention mechanism that cuts latency by 30 % compared to previous versions. A comparison table below highlights key metrics against earlier LTX releases.

Metric	LTX-2.3-fp8	LTX-2.2-fp8
Parameters	7 B	5 B
FP8 Memory	14 GB	10 GB
Inference Latency (ms)	12	18
Throughput (tokens/s)	85	60

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