---
title: LTX 2.3 CPU
emoji: 🎬
colorFrom: indigo
colorTo: pink
sdk: docker
app_port: 7860
pinned: false
license: other
---

# LTX 2.3 CPU — Feasibility Reference + ZeroGPU Recipe

22B-parameter LTX-Video 2.3 (Lightricks) on **free HF CPU** is **not practical**: 2 vCPU + 16 GB RAM cannot host the full pipeline at usable speed. This Space is the **feasibility analysis and upgrade recipe** so any user with a GPU can fork and run instantly.

## TL;DR

| Tier | Hardware | LTX 2.3 distilled-1.1 viable? | Per 2-sec clip |
|---|---|---|---|
| Free CPU | 2 vCPU + 16 GB | ❌ models barely fit at Q3_K_M, ~60-120 min if it even completes | n/a |
| CPU Upgrade | 8 vCPU + 32 GB | ⚠ marginal, ~30-60 min | $0.30/clip |
| ZeroGPU | A100 quota slot | ✅ ~25-40 sec | free w/ Pro |
| GPU L40S | 48 GB VRAM | ✅ ~8 sec | $1/hr |

## Model paths analysed

- **Path A — Unsloth distilled-1.1 Q3_K_M** (`unsloth/LTX-2.3-GGUF` → `distilled-1.1/ltx-2.3-22b-distilled-1.1-Q3_K_M.gguf`, ~10.6 GB). Cleanest 8-step distilled DiT. Best CPU candidate (smallest weights). Requires ComfyUI-GGUF loader.
- **Path C — 10Eros fine-tune + cond_safe distill LoRA** (`vantagewithai/LTX2.3-10Eros-GGUF` + cond_safe LoRA). 10Eros is a *fine-tune*, NOT distilled — README warns *"larger distilled LoRAs will harm the model's fine tune"*. Riskier; needs LoRA tuning. Not a 1:1 replacement for Path A.

Recommendation: **Path A** for the CPU build (smallest, distilled). Path C is preserved here as reference for ZeroGPU forks that have headroom to experiment.

## Text encoder constraint

You **cannot swap** the text encoder. LTX 2.3 was trained with `google/gemma-3-12b-it` — the diffusion U-Net is bound to its embedding space. Smaller/newer LLMs like Qwen3.6-35B-A3B or Gemma-4-E2B-it **will not work** — they produce embeddings in a different distribution.

The only valid lever is **quantising the same encoder smaller**:

| Quant | Size | Quality vs FP16 |
|---|---|---|
| Gemma-3-12B-it Q3_K_M | 6.0 GB | ~98% |
| Gemma-3-12B-it Q4_K_M | 7.4 GB | ~99.5% |
| Gemma-3-12B-it Q5_K_M | 8.6 GB | ~99.9% |

Use `mradermacher/gemma-3-12b-it-qat-abliterated-GGUF` Q3_K_M for the CPU path.

## ZeroGPU fork recipe

Fork this Space to your account, change `sdk: docker` → `sdk: gradio`, change the hardware tier to **ZeroGPU**, and replace `app.py` with the GPU variant in `gpu_app.py`. That's it.

```bash
huggingface-cli repo duplicate WeReCooking/ltx-2.3-cpu YourUsername/ltx-2.3-zerogpu
# Then edit README.md: sdk -> gradio, add: hardware: zerogpu
# Edit Space settings on HF UI -> Hardware -> ZeroGPU
```

## Curl test (once forked to a GPU tier)

```bash
TOKEN="hf_xxx"
SPACE="https://YourUsername-ltx-2-3-zerogpu.hf.space"

EVT=$(curl -s -X POST "$SPACE/gradio_api/call/generate" \
  -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
  -d '{"data":["A woman walking through a neon-lit Tokyo alley at night, cinematic", 2.0, 8]}' \
  | python -c "import sys,json;print(json.load(sys.stdin)['event_id'])")
curl -sN "$SPACE/gradio_api/call/generate/$EVT" -H "Authorization: Bearer $TOKEN"
```

## Logs (SSE)

```bash
curl -N -H "Authorization: Bearer $TOKEN" "https://huggingface.co/api/spaces/WeReCooking/ltx-2.3-cpu/logs/build"
curl -N -H "Authorization: Bearer $TOKEN" "https://huggingface.co/api/spaces/WeReCooking/ltx-2.3-cpu/logs/run"
```

## Why not ship inference on free CPU anyway

I attempted the GGUF path locally. Findings:
- 10.6 GB GGUF DiT + 6 GB GGUF Gemma encoder + VAE + activations = exceeds 16 GB even with sequential offload (load → run → unload pattern). The encoder needs to stay resident during DiT's classifier-free guidance branch (or be re-loaded per step → 50× slower).
- 2 vCPU × 22B params at Q3_K_M ≈ ~120 sec/diffusion step → 8-step distilled = ~16 min just for the DiT loop, plus encode + VAE decode + offload swaps → realistically 60-90 min for a 2-sec, 384×256 clip. HF Space request timeout is 1 hour. The math doesn't close.

The honest path on free CPU is **not to ship a broken Generate button** — instead, ship the recipe and demos.