Lumina-Image 2.0 on Marvell OCTEON TX2 CN9130 NAS (4 GB)

Can the Marvell OCTEON TX2 CN9130 NAS (4 GB) run Lumina-Image 2.0?

No, it doesn’t fit

No. Lumina-Image 2.0 needs about 3.0 GB even at FP8; the Marvell OCTEON TX2 CN9130 NAS (4 GB) has 2.0 GB usable.

Download Lumina-Image 2.0

Original weights · Hugging Face

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

huggingface.co/Alpha-VLLM/Lumina-Image-2.0 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/Alpha-VLLM/Lumina-Image-2.0 (opens in a new tab)

git clone https://github.com/Alpha-VLLM/Lumina-Image-2.0

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 60Good quality (B) · 60 of 100: how capable the model is at FP8, whatever it runs on. Match score 8/100 (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 Marvell OCTEON TX2 CN9130 NAS (4 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.	8.2 / 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.	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	8.2 / 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 Marvell OCTEON TX2 CN9130 NAS (4 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	3.9 GB	2.0 GB	—	—	Won't run	62Good quality (B) · 62 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`	3.0 GB	2.0 GB	—	—	Won't run	60Good quality (B) · 60 of 100: how capable the model is at FP8, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Hardware that can run it

62Good quality (B) · 62 of 100: how capable the model is at FP16, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT FP16 · 9.3 s · Runs great
62Good quality (B) · 62 of 100: how capable the model is at FP16, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER FP16 · 9.9 s · Runs great
62Good quality (B) · 62 of 100: how capable the model is at FP16, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 FP16 · 9.3 s · Runs great
60Good quality (B) · 60 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 GeForce RTX 4070 SUPER FP8 · 12 s · Runs great
62Good quality (B) · 62 of 100: how capable the model is at FP16, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX FP16 · 7.8 s · Runs great
62Good quality (B) · 62 of 100: how capable the model is at FP16, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 FP16 · 7.0 s · Runs great

Models that do run on the Marvell OCTEON TX2 CN9130 NAS (4 GB)

39Basic quality (D) · 39 of 100: how capable the model is at FP8, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. SDXL Turbo FP8 · 73 s
42Fair quality (C) · 42 of 100: how capable the model is at FP16, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking. Realistic Vision V5.1 FP16 · 10.8 min
28Basic quality (D) · 28 of 100: how capable the model is at FP16, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. SD Turbo FP16 · 43 s
40Fair quality (C) · 40 of 100: how capable the model is at FP16, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. DreamShaper 8 FP16 · 10.8 min
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. Stable Diffusion 1.5 FP16 · 10.8 min

Quick answers

How much memory does Lumina-Image 2.0 need on the Marvell OCTEON TX2 CN9130 NAS 4GB?

Even at FP8 it needs about 3.0 GB; the Marvell OCTEON TX2 CN9130 NAS 4GB has 2.0 GB usable.

What can the Marvell OCTEON TX2 CN9130 NAS 4GB run instead?

SDXL Turbo, Realistic Vision V5.1 and SD Turbo run best on it; the page lists more, each with its speed.

What hardware can run Lumina-Image 2.0?

RX 9070 XT 16GB, RTX 4080 SUPER 16GB and RTX 5080 16GB 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.