Wan 2.2 TI2V 5B on Intel Xeon W-2100 series NAS (32 GB)

Can the Intel Xeon W-2100 series NAS (32 GB) run Wan 2.2 TI2V 5B?

Barely: it runs very slowly (tight fit · encoder unloaded after use)

Very slow (tight fit · encoder unloaded after use). At Q4 GGUF it needs about 24.7 GB of 26.2 GB usable, with an estimated speed of 10.6 h.

Best precision: Q4 GGUF
Memory mode: Text encoder offloaded
Time per clip: 10.6 h
Output: 1280×704, 121 frames, 50 steps

Download Wan 2.2 TI2V 5B

Recommended for the Intel Xeon W-2100 series NAS (32 GB): q4_gguf. Every other precision has its own download link in the table below.

Quantized files · GGUF for ComfyUI

Smaller GGUF versions for ComfyUI (with the ComfyUI-GGUF node). No sign-in needed; the model’s license still applies.

Download the q4_gguf (Q4_K_S) file (opens in a new tab)

huggingface.co/QuantStack/Wan2.2-TI2V-5B-GGUF (opens in a new tab)

Original weights · Hugging Face

Full-precision files from Alibaba, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/Wan-AI/Wan2.2-TI2V-5B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Alibaba. The weights themselves download from Hugging Face.

github.com/Wan-Video/Wan2.2 (opens in a new tab)

git clone https://github.com/Wan-Video/Wan2.2

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 65Good quality (B) · 65 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q4 GGUF, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Very slowtight fit · encoder unloaded after use: How well it runs on the Intel Xeon W-2100 series NAS (32 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	29.3 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.	18.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	1.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	15.0 / 15
Match score	63.3 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every precision on the Intel Xeon W-2100 series NAS (32 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	23.8 GB	26.2 GB	10.9 h	Sequential CPU offload	Very slowtight fit · CPU offload	70Great quality (A) · 70 of 100: how capable the model is at FP16, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	22.1 GB	26.2 GB	9.9 h	Sequential CPU offload	Very slowCPU offload	68Good quality (B) · 68 of 100: how capable the model is at FP8, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`Q8 GGUF`	22.2 GB	26.2 GB	12.6 h	Sequential CPU offload	Very slowCPU offload	69Good quality (B) · 69 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4 GGUF`	24.7 GB	26.2 GB	10.6 h	Text encoder offloaded	Very slowtight fit · encoder unloaded after use	65Good quality (B) · 65 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)

Other hardware that runs it

70Great quality (A) · 70 of 100: how capable the model is at FP16, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell FP16 · 6.4 min · Runs well
65Good quality (B) · 65 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Q4 GGUF · 8.7 min · Runs well (tight fit)
68Good quality (B) · 68 of 100: how capable the model is at FP8, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 FP8 · 10.1 min · Runs well (tight fit · CPU offload)
68Good quality (B) · 68 of 100: how capable the model is at FP8, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX FP8 · 11.3 min · Runs well (tight fit · CPU offload)
68Good quality (B) · 68 of 100: how capable the model is at FP8, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA L40S FP8 · 7.2 min · Runs well
68Good quality (B) · 68 of 100: how capable the model is at FP8, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX 6000 Ada Generation FP8 · 7.2 min · Runs well

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.