Wan2.2-T2V-A14B-Diffusers GPU Requirements: VRAM & Cheapest GPU
Wan2.2-T2V-A14B-Diffusers has about 14.3B parameters. See exactly how much GPU memory it needs at FP16, INT8, and INT4, and the cheapest GPU to run it, with live hourly pricing from 5+ data center partners.
- Parameters
- 14.3B
- Min VRAM (INT4)
- 7.8 GB
- Cheapest (INT8)
- $0.48/hr
- Deploy
- < 2 min
To run Wan2.2-T2V-A14B-Diffusers for inference at FP16, you need roughly 31 GB of VRAM. The cheapest fit on Spheron AI is RTX 6000 Ada 48GB at about $0.78/hr. Quantize to INT4 to run it on a smaller, cheaper GPU.
VRAM required
Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.
- 01$0.78/hr$0.78/hr1× RTX 6000 Ada 48GBCHEAPESTAda Lovelace · GDDR6 ECC
- 02$0.78/hr$0.78/hr1× RTX 5090 32GBBlackwell · GDDR7
- 03$0.96/hr$0.96/hr1× L40S 48GBAda Lovelace · GDDR6
- 04$0.48/hr$0.96/hr2× RTX 4090 24GBAda Lovelace · GDDR6X
- 05$1.43/hr$1.43/hr1× A100 80GBAmpere · HBM2e
Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.
VRAM required to run Wan2.2-T2V-A14B-Diffusers
Estimated peak VRAM at context length 4,096 and batch size 1, including weights, activations, and KV cache. Quantizing to INT8 (Q8) or INT4 (Q4) cuts memory roughly in half and in quarter.
| Precision | Inference | LoRA fine-tune | Full fine-tune |
|---|---|---|---|
| FP16 | 31 GB | 47 GB | 125 GB |
| INT8 | 16 GB | 23 GB | 62 GB |
| INT4 | 7.8 GB | 12 GB | 31 GB |
Cheapest GPU to run Wan2.2-T2V-A14B-Diffusers by precision
31GB
Full precision. Best quality, highest memory.
16GB
8-bit quantized. ~2x smaller, minimal quality loss.
7.8GB
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning Wan2.2-T2V-A14B-Diffusers
Inference only holds the model weights plus a KV cache, so it is the cheapest setup. LoRA fine-tuning adds a small adapter and roughly 50% more memory. Full fine-tuning holds gradients and optimizer state on top of the weights, which is about 4x the inference footprint, so it often needs multiple GPUs even when inference fits on one. For Wan2.2-T2V-A14B-Diffusers, an on-demand RTX 6000 Ada 48GB instance covers inference and LoRA, while a full fine-tune needs several times that memory and often spans multiple GPUs. Check the live GPU pricing for current rates.
Deployment guideDeploy Wan 2.1 step by stepHands-on production setup, GPU configs, and benchmarks for Wan2.2-T2V-A14B-Diffusers.Read guideSimilar models
Compare GPU requirements for models in the same class.
14.3BQuantStack/Wan2.2-T2V-A14B-GGUF
14.3BWan-AI/Wan2.1-T2V-14B-Diffusers
14.3BWan-AI/Wan2.1-T2V-14B
14.3Bmagespace/Wan2.2-I2V-A14B-Lightning-Diffusers
19.0BLightricks/LTX-2.5-Diffusers
21.0BChrisColeTech/LTX-2.3-uncensored-v1.4-FP8
Wan2.2-T2V-A14B-Diffusers GPU questions
VRAM, GPU choice and cost questions for running Wan2.2-T2V-A14B-Diffusers on cloud GPUs.
Wan2.2-T2V-A14B-Diffusers has about 14.3B parameters. At FP16 it needs roughly 31 GB of VRAM for inference, including weights, activations, and KV cache. Quantized to INT4 that drops to around 7.8 GB. Leave about 10% headroom for production traffic.
For FP16 inference, the cheapest fit on Spheron AI is RTX 6000 Ada 48GB at about $0.78/hr. If you quantize to INT4, RTX 4090 24GB at about $0.48/hr runs it for less. Pricing is aggregated live from 5+ data center partners with per-minute billing.
LoRA fine-tuning adds roughly 50% on top of inference memory, so it usually fits the same class of GPU. Full fine-tuning holds gradients and optimizer state and needs about 4x the inference VRAM, which often means multiple GPUs for Wan2.2-T2V-A14B-Diffusers. The VRAM matrix above shows the exact estimate for each setup.
We read the parameter count directly from the model's safetensors metadata on HuggingFace, then estimate peak VRAM from weights, activations, KV cache, and framework overhead at your chosen precision. The estimate lands within about 15% of real-world use for most transformer models.
Quantized to INT4, Wan2.2-T2V-A14B-Diffusers needs about 7.8 GB of VRAM, so it fits on a 24 GB RTX 4090 with room for a small batch. At FP16 it needs roughly 31 GB, which usually means a data center GPU. The precision picks above list the cheapest GPU that fits each setup.
Yes. At FP16 Wan2.2-T2V-A14B-Diffusers needs about 31 GB of VRAM, which fits one 80 GB H100 with about 49 GB left for longer contexts and larger batches. The precision picks above show the cheapest GPU that fits each setup on Spheron AI.
Wan2.2-T2V-A14B-Diffusers needs one 80 GB GPU at FP16 (about 31 GB of VRAM), one 80 GB GPU at INT8 (16 GB) and one 80 GB GPU at INT4 (7.8 GB). All of these fit inside a single 8-GPU node, so no multi-node setup is needed for inference. Smaller cards such as 48 GB or 24 GB GPUs need proportionally more of them.
Wan2.2-T2V-A14B-Diffusers needs about 16 GB of VRAM at INT8, roughly half its FP16 footprint of 31 GB. The cheapest INT8 fit on Spheron AI is RTX 4090 24GB at about $0.48/hr. INT8 is the usual middle ground: half the memory of FP16, with a smaller quality drop than 4-bit quantization.
Running Wan2.2-T2V-A14B-Diffusers around the clock at FP16 costs about $569 a month on RTX 6000 Ada 48GB (730 hours at $0.78/hr). Quantized to INT4 it costs about $350 a month on RTX 4090 24GB (730 hours at $0.48/hr). Spheron AI bills per minute, so stopping the instance when it is idle cuts that figure directly.
Wan2.2-T2V-A14B-Diffusers needs about 31 GB at FP16, 16 GB at INT8 and 7.8 GB at INT4. Use FP16 when output quality matters most or you plan to fine-tune, INT8 for most production inference, and INT4 when cost or GPU size is the constraint: the cheapest INT4 fit is RTX 4090 24GB at about $0.48/hr.