SkyReels V2 T2V 14B 540P on LattePanda Sigma (16 GB)

Can the LattePanda Sigma (16 GB) run SkyReels V2 T2V 14B 540P?

No, it doesn’t fit

No. SkyReels V2 T2V 14B 540P needs about 14.2 GB even at FP8; the LattePanda Sigma (16 GB) has 8.0 GB usable.

Download SkyReels V2 T2V 14B 540P

Original weights · Hugging Face

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

huggingface.co/Skywork/SkyReels-V2-T2V-14B-540P (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/SkyworkAI/SkyReels-V2 (opens in a new tab)

git clone https://github.com/SkyworkAI/SkyReels-V2

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 72Great quality (A) · 72 of 100: how capable the model is at FP8, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at FP8, 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 Won't run: How well it runs on the LattePanda Sigma (16 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.	9.8 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.	0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	0.0 / 15
Match score	9.8 / 100

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

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Every precision on the LattePanda Sigma (16 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	19.1 GB	8.0 GB	—	—	Won't run	74Great quality (A) · 74 of 100: how capable the model is at FP16, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	14.2 GB	8.0 GB	—	—	Won't run	72Great quality (A) · 72 of 100: how capable the model is at FP8, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Hardware that can run it

72Great quality (A) · 72 of 100: how capable the model is at FP8, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 FP8 · 17.6 min · Slow (tight fit)
74Great quality (A) · 74 of 100: how capable the model is at FP16, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell FP16 · 17.1 min · Slow
74Great quality (A) · 74 of 100: how capable the model is at FP16, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 FP16 · 30.3 min · Slow (CPU offload)
74Great quality (A) · 74 of 100: how capable the model is at FP16, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX FP16 · 33.8 min · Slow (CPU offload)
72Great quality (A) · 72 of 100: how capable the model is at FP8, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT FP8 · 36.2 min · Slow (tight fit · CPU offload)
72Great quality (A) · 72 of 100: how capable the model is at FP8, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 FP8 · 36.4 min · Slow (tight fit · CPU offload)

Models that do run on the LattePanda Sigma (16 GB)

49Fair quality (C) · 49 of 100: how capable the model is at FP8, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. LTX-Video 2B FP8 · 7.3 min
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. AnimateDiff v3 (SD 1.5) FP16 · 6.2 min
44Fair quality (C) · 44 of 100: how capable the model is at FP8, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 2B FP8 · 17.5 min
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.1 T2V 1.3B FP8 · 69.9 min
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 5B FP8 · 64.9 min

Quick answers

How much memory does SkyReels V2 T2V 14B 540P need on the LattePanda Sigma 16GB?

Even at FP8 it needs about 14.2 GB; the LattePanda Sigma 16GB has 8.0 GB usable.

What can the LattePanda Sigma 16GB run instead?

LTX-Video 2B, AnimateDiff v3 (SD 1.5) and CogVideoX 2B run best on it; the page lists more, each with its speed.

What hardware can run SkyReels V2 T2V 14B 540P?

RTX 5090 32GB, NVIDIA RTX PRO 6000 Blackwell 96GB and RTX 4090 24GB run it; the page lists more, each with its speed.

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.