Ideogram 4 on Marvell OCTEON TX2 CN9130 NAS (32 GB)

Can the Marvell OCTEON TX2 CN9130 NAS (32 GB) run Ideogram 4?

Barely: it runs very slowly

Very slow. At NF4 it needs about 21.8 GB of 26.2 GB usable and takes about 3.2 h per image.

Best precision: NF4
Memory mode: Everything on GPU
Time per image: 3.2 h
Output: 1024×1024, 48 steps

Download Ideogram 4

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.

huggingface.co/leejet/ideogram-4-GGUF (opens in a new tab)

Original weights · Hugging Face

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

huggingface.co/ideogram-ai/ideogram-4-fp8 (opens in a new tab)

Free Hugging Face account needed: accept the license on the model page and the download starts right away.

Official repo · GitHub

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

github.com/ideogram-oss/ideogram4 (opens in a new tab)

git clone https://github.com/ideogram-oss/ideogram4

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

How it’s rated

Quality 85Excellent quality (A+) · 85 of 100: how capable the model is at NF4, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at NF4, 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 slow: How well it runs on the Marvell OCTEON TX2 CN9130 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.	38.5 / 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.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	0.4 / 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	78.9 / 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 Marvell OCTEON TX2 CN9130 NAS (32 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	15.4 GB	26.2 GB	3.7 h	Sequential CPU offload	Very slowCPU offload	92Excellent quality (A+) · 92 of 100: how capable the model is at FP16, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	21.2 GB	26.2 GB	2.7 h	Text encoder offloaded	Very slowencoder unloaded after use	90Excellent quality (A+) · 90 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.	Original weights (opens in a new tab)
`Q4 GGUF`	22.5 GB	26.2 GB	3.5 h	Everything on GPU	Very slowtight fit	85Excellent quality (A+) · 85 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.	GGUF (opens in a new tab)
`NF4`	21.8 GB	26.2 GB	3.2 h	Everything on GPU	Very slow	85Excellent quality (A+) · 85 of 100: how capable the model is at NF4, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Other hardware that runs it

92Excellent quality (A+) · 92 of 100: how capable the model is at FP16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell FP16 · 18 s · Runs well
90Excellent quality (A+) · 90 of 100: how capable the model is at FP8, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 FP8 · 19 s · Runs well (tight fit)
85Excellent quality (A+) · 85 of 100: how capable the model is at NF4, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 NF4 · 28 s · Runs well (tight fit)
85Excellent quality (A+) · 85 of 100: how capable the model is at NF4, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX NF4 · 31 s · Runs well (tight fit)
85Excellent quality (A+) · 85 of 100: how capable the model is at NF4, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT NF4 · 38 s · Runs well (encoder in RAM)
85Excellent quality (A+) · 85 of 100: how capable the model is at NF4, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 NF4 · 38 s · Runs well (encoder in RAM)

Quick answers

How much memory does Ideogram 4 need on the Marvell OCTEON TX2 CN9130 NAS 32GB?

At NF4 it needs about 21.8 GB. The Marvell OCTEON TX2 CN9130 NAS 32GB has 26.2 GB usable.

How long does Ideogram 4 take on the Marvell OCTEON TX2 CN9130 NAS 32GB?

About 3.2 h per image at NF4 and default settings, an estimate scaled from a reference timing by this hardware’s compute 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.