LTX 2.3 is a 22-billion-parameter video generation model — substantially larger than WAN 2.2's 14B. The full FP16 weights are 44 GB; an FP8 quantization brings that down to 22 GB; GGUF Q4 variants land around 13 GB. What you can run on your card depends on which quant you pick and how much VRAM headroom is left after the text encoder, video VAE, audio VAE, and any LoRAs are loaded. This guide covers the practical numbers, GPU by GPU.
If you don't have a capable GPU and don't want to spend money on one, our AI Porn Generator runs LTX 2.3 on rented 48 GB cards in your browser — 160 free tokens daily, no setup. For local power users, the ComfyUI Workflow Pack ships every workflow plus the right quants for different VRAM budgets. Discord support included.
VRAM Required by Quant
| Quant | Model size | Loaded VRAM (model only) | Total VRAM (with encoder + VAE + LoRAs) |
|---|---|---|---|
| FP16 | 44 GB | ~46 GB | ~50 GB+ |
| BF16 | 44 GB | ~46 GB | ~50 GB+ |
| FP8 (e4m3fn) | 22 GB | ~24 GB | ~32 GB |
| NVFP4 (RTX 5090) | 11 GB | ~13 GB | ~22 GB |
| GGUF Q8 | 23 GB | ~25 GB | ~32 GB |
| GGUF Q4_K_M | 13 GB | ~15 GB | ~22 GB |
“Total VRAM” assumes the Gemma 3 12B text encoder is loaded in FP8 (~6 GB), plus video VAE (~1 GB), audio VAE (~1 GB), and a typical 4–LoRA stack (~2 GB). Workflows with force_offload=true can push the encoder to system RAM after use, which trims another 6 GB.
GPU-by-GPU Breakdown
RTX 5090 (32 GB) — Best Consumer Card
32 GB is the sweet spot for LTX 2.3. The 5090 runs FP8 comfortably with full text encoder loaded, and the Blackwell architecture has native NVFP4 support — the smallest and fastest LTX 2.3 quant. Generation time for a 12-second clip: 4–6 minutes. Recommended.
RTX 4090 (24 GB) — Tight but Workable
24 GB runs FP8 if you offload the text encoder after encoding (set force_offload=true on the encoder node). GGUF Q4_K_M runs comfortably with full encoder resident. Expect 6–10 minutes per 12-second clip. Avoid loading two LoRAs over rank 64 simultaneously — VRAM gets tight.
RTX 3090 (24 GB) — Same VRAM, Slower
Same 24 GB as the 4090 so the same quants fit. Older Ampere architecture means ~50% slower than 4090 — expect 10–15 minutes per clip. Still very usable.
RTX 4080 / 5080 (16 GB) — GGUF Only
16 GB cards need GGUF Q4_K_M with text encoder offloaded to RAM. Possible but tight; you'll hit OOM if you try to stack heavy LoRAs. Better suited for shorter clips (under 6 seconds) or lower resolutions (768 longest side).
RTX 4070 / 4070 Ti / 3080 (12 GB) — Marginal
12 GB is below the practical floor. You can technically load Q4_K_M with aggressive offloading, but you'll be system-RAM-bound and very slow (20+ minutes per clip), and any LoRA you add will OOM. Consider WAN 2.2 instead — it runs comfortably on 12 GB. See LTX 2.3 vs WAN 2.2.
8 GB and below — Not Practical
LTX 2.3 will not run usefully on 8 GB or less. Even with the smallest quant, the runtime activations exceed available VRAM. Use a cloud GPU or our hosted Studio instead.
Workstation / Datacenter Cards
- A6000 (48 GB) — Runs FP16/BF16 comfortably. 5–8 min per clip. Quiet, ideal for sustained workloads.
- A100 (40/80 GB) — Excellent. 80 GB lets you run multiple jobs in parallel.
- H100 / H200 — Overkill for single jobs but great for batch generation pipelines.
- RTX PRO 6000 (96 GB) — Workstation card with massive headroom; what we use for 4× parallel LTX 2.3 jobs in the Studio.
LTX 2.3 on Mac?
Apple Silicon support exists through MLX-based ports and Draw Things, but it's slow and feature-incomplete relative to NVIDIA. M3 Max / M4 Pro can run a Q4 variant via Draw Things at roughly 2–3× the runtime of a 4090. Audio generation and lipsync are not yet supported on the Mac path. Detail in LTX 2.3 on Mac.
If Your GPU Can't Handle It
Three options ranked by cost-effectiveness:
- VirtuaVixen Studio — free 160 tokens daily, no GPU, NSFW-friendly. Best for casual users.
- RunPod 4090 / A6000 — $0.30–0.80 per hour, you SSH in and use ComfyUI. Best for batch work.
- Buy more VRAM — used 3090s are around $700–900 and run LTX 2.3 fine. Worth it if you generate every day.
Try Before You Upgrade
Before spending on a new GPU, run LTX 2.3 in our browser-based Studio for free and decide if it fits your workflow. If you decide to self-host, our ComfyUI Workflow Pack includes pre-tuned workflows for 24 GB and 48 GB GPUs (different quant choices, different LoRA loadouts) — saves you the trial-and-error of dialing in your hardware.
Recommended Pre-Built Rigs for LTX 2.3
Building a PC from parts is a full weekend. If you'd rather just have a working machine arrive at your door, here are pre-built rigs from Amazon that we'd recommend for LTX 2.3 specifically — chosen for the right balance of GPU VRAM, system RAM (matters for text-encoder offload), and NVMe storage (matters because the model weights are 60+ GB).
VirtuaVixen earns a small commission on Amazon purchases through these links, at no extra cost to you. As an Amazon Associate we earn from qualifying purchases.
Best Overall — RTX 5090 / 32 GB VRAM
Mantis V2 — RTX 5090, Ryzen 9 9950X3D, 64 GB DDR5, 2 TB NVMe Gen4 — Best balance for LTX 2.3 in 2026. The 5090's 32 GB VRAM runs FP8 with full text encoder resident; native NVFP4 support cuts another 30% off generation time. 64 GB system RAM gives plenty of room to offload the encoder when running heavy LoRA stacks. Expect 4–6 minutes per 12-second LTX clip. 3-year warranty.
Power User — RTX 5090 + 96 GB RAM
Mantis V2 — RTX 5090, Ryzen 7 9800X3D, 96 GB DDR5, 4 TB NVMe Gen4 — If you plan to train LoRAs (see LoRA training guide) or run LTX 2.3 alongside other models, the 96 GB system RAM is a real upgrade. The 4 TB NVMe holds multiple model variants without juggling. Same 5090 GPU as above.
Sweet Spot — RTX 4090 / 24 GB VRAM
Skytech Prism — RTX 4090, Intel i7 14700K, 64 GB DDR5, 2 TB NVMe Gen4 — The 4090 is still the best price-per-token for LTX 2.3. 24 GB VRAM runs FP8 with text-encoder offload (set force_offload=true); 6–10 min per 12-second clip. 64 GB system RAM is the right amount for offloading without thrashing. Skytech's build quality and US-based support are solid.
Budget RTX 4090
Skytech Prism — RTX 4090, Ryzen 7 7800X3D, 32 GB DDR5, 2 TB NVMe Gen4 — Same 4090 GPU and 2 TB NVMe but with a 7800X3D and 32 GB RAM. 32 GB is the absolute floor for LTX 2.3 — workable but tight. Worth a few hundred less than the 64 GB build if budget matters.
Workstation-Style Build
Empowered PC Panorama — RTX 4090, Ryzen 7 5700X3D, 64 GB RAM, 1 TB NVMe + 3 TB HDD — Slightly older CPU (5700X3D) but the 4090 GPU is what does the LTX 2.3 work, and 64 GB RAM + the 3 TB HDD for output archive is a great content-creator setup. Often discounted vs the newer 7800X3D builds.
What Else You Need
- System RAM: 32 GB minimum, 64 GB recommended. LTX 2.3's text encoder offloads to system RAM during generation.
- NVMe storage: at least 2 TB. The full LTX 2.3 model + abliterated encoder + LoRAs is ~60 GB. You'll want headroom for multiple variants.
- PSU: 1000 W gold for 4090, 1200 W for 5090. All builds above ship with appropriate PSUs.
- Cooling: pre-builts ship with 360mm AIO. LTX 2.3 generation runs the GPU hard — air cooling is borderline.
Or skip the hardware entirely — our Studio runs LTX 2.3 on rented 48 GB GPUs with 160 free daily tokens. No purchase, no setup. The math: a $2,500 rig pays itself back vs cloud rental in about 2,000 generations. Below that, cloud (or our Studio) is cheaper.
