ERNIE-Image Turbo on Marvell ARMADA 8040 NAS (16 GB)

Can the Marvell ARMADA 8040 NAS (16 GB) run ERNIE-Image Turbo?

Barely: it runs very slowly (tight fit)

Very slow (tight fit). At Q4 GGUF it needs about 11.3 GB of 12.0 GB usable and takes about 26.5 min per image.

Best precision: Q4 GGUF
Memory mode: Everything on GPU
Time per image: 26.5 min
Output: 1024×1024, 8 steps

Download ERNIE-Image Turbo

Recommended for the Marvell ARMADA 8040 NAS (16 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/unsloth/ERNIE-Image-Turbo-GGUF (opens in a new tab)

Original weights · Hugging Face

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

huggingface.co/baidu/ERNIE-Image-Turbo (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/baidu/ERNIE-Image (opens in a new tab)

git clone https://github.com/baidu/ERNIE-Image

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 79Great quality (A) · 79 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 74/100 (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: How well it runs on the Marvell ARMADA 8040 NAS (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.	35.6 / 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.	23.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	1.2 / 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	74.8 / 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 ARMADA 8040 NAS (16 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	8.1 GB	12.0 GB	28.7 min	Sequential CPU offload	Very slowCPU offload	85Excellent quality (A+) · 85 of 100: how capable the model is at FP16, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	10.6 GB	12.0 GB	20.9 min	Text encoder offloaded	Very slowtight fit · encoder unloaded after use	83Great quality (A) · 83 of 100: how capable the model is at FP8, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`Q8 GGUF`	11.2 GB	12.0 GB	26.7 min	Text encoder offloaded	Very slowtight fit · encoder unloaded after use	84Great quality (A) · 84 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4 GGUF`	11.3 GB	12.0 GB	26.5 min	Everything on GPU	Very slowtight fit	79Great quality (A) · 79 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)

Other hardware that runs it

85Excellent quality (A+) · 85 of 100: how capable the model is at FP16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 FP16 · 2.7 s · Runs great
84Great quality (A) · 84 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX Q8 GGUF · 4.1 s · Runs great
84Great quality (A) · 84 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 Q8 GGUF · 6.9 s · Runs great
84Great quality (A) · 84 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 Q8 GGUF · 3.7 s · Runs great
84Great quality (A) · 84 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT Q8 GGUF · 4.9 s · Runs well (tight fit)
84Great quality (A) · 84 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 Ti 16GB Q8 GGUF · 9.9 s · Runs well (tight fit)

Quick answers

How much memory does ERNIE-Image Turbo need on the Marvell ARMADA 8040 NAS 16GB?

At Q4 GGUF it needs about 11.3 GB. The Marvell ARMADA 8040 NAS 16GB has 12.0 GB usable.

How long does ERNIE-Image Turbo take on the Marvell ARMADA 8040 NAS 16GB?

About 26.5 min per image at Q4 GGUF 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.