CogVideoX 1.5 5B on LattePanda Sigma (16 GB)

Can the LattePanda Sigma (16 GB) run CogVideoX 1.5 5B?

No, it doesn’t fit

No. CogVideoX 1.5 5B needs about 31.2 GB even at FP8; the LattePanda Sigma (16 GB) has 8.0 GB usable.

Download CogVideoX 1.5 5B

Original weights · Hugging Face

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

huggingface.co/zai-org/CogVideoX1.5-5B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/zai-org/CogVideo (opens in a new tab)

git clone https://github.com/zai-org/CogVideo

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

How it’s rated

Quality 58Good quality (B) · 58 of 100: how capable the model is at FP8, whatever it runs on. Match score 7/100 (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.	7.9 / 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	7.9 / 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`	33.1 GB	8.0 GB	—	—	Won't run	60Good quality (B) · 60 of 100: how capable the model is at FP16, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	31.2 GB	8.0 GB	—	—	Won't run	58Good quality (B) · 58 of 100: how capable the model is at FP8, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Hardware that can run it

60Good quality (B) · 60 of 100: how capable the model is at FP16, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell FP16 · 11.0 min · Runs well
60Good quality (B) · 60 of 100: how capable the model is at FP16, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H100 80GB SXM FP16 · 10.4 min · Runs well
60Good quality (B) · 60 of 100: how capable the model is at FP16, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H800 80GB SXM FP16 · 10.4 min · Runs well
60Good quality (B) · 60 of 100: how capable the model is at FP16, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GH200 Grace Hopper (96 GB GPU) FP16 · 10.4 min · Runs well
60Good quality (B) · 60 of 100: how capable the model is at FP16, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H100 NVL FP16 · 11.3 min · Runs well
60Good quality (B) · 60 of 100: how capable the model is at FP16, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell Server Edition FP16 · 11.3 min · Runs well

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 CogVideoX 1.5 5B need on the LattePanda Sigma 16GB?

Even at FP8 it needs about 31.2 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 CogVideoX 1.5 5B?

NVIDIA RTX PRO 6000 Blackwell 96GB, NVIDIA H100 80GB SXM and NVIDIA H800 80GB SXM 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.