AuraFlow v0.3 on UP Squared Pro 7000 (4 GB)

Can the UP Squared Pro 7000 (4 GB) run AuraFlow v0.3?

No, it doesn’t fit

No. AuraFlow v0.3 needs about 3.7 GB even at Q4 GGUF; the UP Squared Pro 7000 (4 GB) has 2.0 GB usable.

Download AuraFlow v0.3

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_S) file (opens in a new tab)

huggingface.co/city96/AuraFlow-v0.3-gguf (opens in a new tab)

Original weights · Hugging Face

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

huggingface.co/fal/AuraFlow-v0.3 (opens in a new tab)

No sign-in needed.

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

How it’s rated

Quality 53Fair quality (C) · 53 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 7/100 (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 UP Squared Pro 7000 (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.	7.3 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; 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	7.3 / 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 UP Squared Pro 7000 (4 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	7.0 GB	2.0 GB	—	—	Won't run	58Good quality (B) · 58 of 100: how capable the model is at FP16, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	4.6 GB	2.0 GB	—	—	Won't run	56Good quality (B) · 56 of 100: how capable the model is at FP8, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`Q8 GGUF`	4.8 GB	2.0 GB	—	—	Won't run	57Good quality (B) · 57 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4 GGUF`	3.7 GB	2.0 GB	—	—	Won't run	53Fair quality (C) · 53 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)

Hardware that can run it

56Good quality (B) · 56 of 100: how capable the model is at FP8, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT FP8 · 17 s · Runs well
56Good quality (B) · 56 of 100: how capable the model is at FP8, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 FP8 · 17 s · Runs well
56Good quality (B) · 56 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. NVIDIA GeForce RTX 4080 SUPER FP8 · 18 s · Runs well
56Good quality (B) · 56 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. NVIDIA GeForce RTX 4070 Ti SUPER FP8 · 20 s · Runs well
56Good quality (B) · 56 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. NVIDIA GeForce RTX 5070 Ti FP8 · 20 s · Runs well
58Good quality (B) · 58 of 100: how capable the model is at FP16, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 FP16 · 26 s · Runs well

Models that do run on the UP Squared Pro 7000 (4 GB)

39Basic quality (D) · 39 of 100: how capable the model is at FP8, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. SDXL Turbo FP8 · 7.2 s
28Basic quality (D) · 28 of 100: how capable the model is at FP16, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. SD Turbo FP16 · 4.3 s
30Basic quality (D) · 30 of 100: how capable the model is at FP16, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. LCM DreamShaper v7 FP16 · 13 s
42Fair quality (C) · 42 of 100: how capable the model is at FP16, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking. Realistic Vision V5.1 FP16 · 64 s
40Fair quality (C) · 40 of 100: how capable the model is at FP16, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. DreamShaper 8 FP16 · 64 s

Quick answers

How much memory does AuraFlow v0.3 need on the UP Squared Pro 7000 4GB?

Even at Q4 GGUF it needs about 3.7 GB; the UP Squared Pro 7000 4GB has 2.0 GB usable.

What can the UP Squared Pro 7000 4GB run instead?

SDXL Turbo, SD Turbo and LCM DreamShaper v7 run best on it; the page lists more, each with its speed.

What hardware can run AuraFlow v0.3?

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