Suba Neon

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

Make sure to follow the instructions below.

No manual effort needed; the setup auto-ingests the large data.

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

🧩 Hash sum → 395608ab7258364023c9f4e2de9b034f — Update date: 2026-06-24 Verify Processor: high single-core performance needed for token latency RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: 100 GB for multi-modal model vision components Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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.

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