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Can the NVIDIA RTX A400 run MOSS-Image SFT?

76Great quality (A) · 76 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly (CPU offload)

Very slow (CPU offload). At Q4 GGUF it needs about 3.3 GB of 4.0 GB usable and takes about 4.7 min per image.

Best precision
Q4 GGUF
Memory mode
Sequential CPU offload
Time per image
4.7 min
Output
1024×1024, 50 steps

Best app to run it on the NVIDIA RTX A400

On Windows

ComfyUI

Desktop app · runs on PyTorch (ComfyUI), GGUF via ComfyUI-GGUF

  • Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Get ComfyUI

On Linux

ComfyUI

Desktop app · runs on PyTorch (ComfyUI), GGUF via ComfyUI-GGUF

  • Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Get ComfyUI

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download MOSS-Image SFT

Recommended for the NVIDIA RTX A400: 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 2 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 64/100 (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 slowCPU offload
How well it runs on the NVIDIA RTX A400: 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.34.3 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.12.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.2.8 / 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 score64.1 / 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 NVIDIA RTX A400

Quant Memory needed Available Speed Memory mode Runs Quality Download
FP16 5.8 GB 4.0 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 4.0 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 4.0 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 4.0 GB 4.7 min Sequential CPU offload Very slowCPU offload 76Great quality (A) · 76 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)

Other hardware that runs it

Quick answers

How much memory does MOSS-Image SFT need on the NVIDIA RTX A400 4GB?

At Q4 GGUF it needs about 3.3 GB. The NVIDIA RTX A400 4GB has 4.0 GB usable.

How long does MOSS-Image SFT take on the NVIDIA RTX A400 4GB?

About 4.7 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.

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