Full Deployment LTX-2.3-fp8 Using Pinokio Quantized GGUF Easy Build

Full Deployment LTX-2.3-fp8 Using Pinokio Quantized GGUF Easy Build

The fastest way to get this model running locally is via Docker.

Make sure to follow the instructions below.

The setup auto-streams the model assets (expect a multi-GB download).

The smart installation system will instantly find the perfect configuration for your specific hardware.

📎 HASH: 871b42d0a301ada33149fb39e844f6bd | Updated: 2026-06-28



  • Processor: Intel i5 or AMD Ryzen 5 for basic 7B models
  • RAM: at least 32 GB in dual-channel mode for bandwidth
  • Disk Space: required: fast PCIe 4.0 drive for instant boots
  • Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading

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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