目錄Retrievers

The fastest method for installing this model locally is by using Docker.

Follow the guidelines below to continue.

1-click setup: the app automatically fetches the large weight files.

You don't need to tweak anything, as the installer will automatically pick the highest performing setup for you.

📘 Build Hash: 5f97c846d7ef22b8fa44af0773fb3e9a • 🗓 2026-06-22 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: at least 100 GB for multiple local LLM variants Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

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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