MOSS-Image SFT on Marvell ARMADA 8040 NAS (4 GB)

Can the Marvell ARMADA 8040 NAS (4 GB) run MOSS-Image SFT?

No, it doesn’t fit

No. MOSS-Image SFT needs about 3.3 GB even at Q4 GGUF; the Marvell ARMADA 8040 NAS (4 GB) has 2.0 GB usable.

Download MOSS-Image SFT

Recommended: 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_M) file (opens in a new tab)

huggingface.co/unsloth/MOSS-Image-SFT-GGUF (opens in a new tab)

Original weights · Hugging Face

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

huggingface.co/OpenMOSS-Team/MOSS-Image-SFT (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/OpenMOSS/MOSS-Image (opens in a new tab)

git clone https://github.com/OpenMOSS/MOSS-Image

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

How it’s rated

Quality 76Great quality (A) · 76 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 10/100 (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 Won't run: How well it runs on the Marvell ARMADA 8040 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.	10.3 / 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	10.3 / 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 ARMADA 8040 NAS (4 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	5.8 GB	2.0 GB	—	—	Won't run	82Great quality (A) · 82 of 100: how capable the model is at FP16, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	4.0 GB	2.0 GB	—	—	Won't run	80Great quality (A) · 80 of 100: how capable the model is at FP8, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`Q8 GGUF`	4.1 GB	2.0 GB	—	—	Won't run	81Great quality (A) · 81 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4 GGUF`	3.3 GB	2.0 GB	—	—	Won't run	76Great quality (A) · 76 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)

Hardware that can run it

80Great quality (A) · 80 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 4090 FP8 · 13 s · Runs great
80Great quality (A) · 80 of 100: how capable the model is at FP8, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX FP8 · 14 s · Runs well
80Great quality (A) · 80 of 100: how capable the model is at FP8, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 FP8 · 11 s · Runs great
80Great quality (A) · 80 of 100: how capable the model is at FP8, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 FP8 · 24 s · Runs well
82Great quality (A) · 82 of 100: how capable the model is at FP16, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell FP16 · 10 s · Runs great
82Great quality (A) · 82 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 · 20 s · Runs well (encoder in RAM)

Models that do run on the Marvell ARMADA 8040 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 MOSS-Image SFT need on the Marvell ARMADA 8040 NAS 4GB?

Even at Q4 GGUF it needs about 3.3 GB; the Marvell ARMADA 8040 NAS 4GB has 2.0 GB usable.

What can the Marvell ARMADA 8040 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 MOSS-Image SFT?

RTX 4090 24GB, RX 7900 XTX 24GB and RTX 5090 32GB 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.