HunyuanImage 3.0 (80B-A13B) on NVIDIA RTX 6000 Ada Generation

Can the NVIDIA RTX 6000 Ada Generation run HunyuanImage 3.0 (80B-A13B)?

Yes, but slowly (CPU offload)

Slow (CPU offload). At FP8 it needs about 32.8 GB of 48.0 GB usable and takes about 65 s per image.

Best precision: FP8
Memory mode: Sequential CPU offload
Time per image: 65 s
Output: 1024×1024, 50 steps

Download HunyuanImage 3.0 (80B-A13B)

Original weights · Hugging Face

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

huggingface.co/tencent/HunyuanImage-3.0 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/Tencent-Hunyuan/HunyuanImage-3.0 (opens in a new tab)

git clone https://github.com/Tencent-Hunyuan/HunyuanImage-3.0

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 88Excellent quality (A+) · 88 of 100: how capable the model is at FP8, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at FP8, 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 SlowCPU offload: How well it runs on the NVIDIA RTX 6000 Ada Generation: 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.	39.7 / 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.	5.9 / 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	72.6 / 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 6000 Ada Generation

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	61.8 GB	48.0 GB	—	—	Won't run	90Excellent quality (A+) · 90 of 100: how capable the model is at FP16, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	32.8 GB	48.0 GB	65 s	Sequential CPU offload	SlowCPU offload	88Excellent quality (A+) · 88 of 100: how capable the model is at FP8, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Other hardware that runs it

88Excellent quality (A+) · 88 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 RTX PRO 6000 Blackwell FP8 · 40 s · Runs well (tight fit)
88Excellent quality (A+) · 88 of 100: how capable the model is at FP8, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H200 141GB SXM FP8 · 38 s · Runs well
88Excellent quality (A+) · 88 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 GH200 Grace Hopper (96 GB GPU) FP8 · 38 s · Runs well (tight fit)
88Excellent quality (A+) · 88 of 100: how capable the model is at FP8, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA B200 FP8 · 33 s · Runs well
88Excellent quality (A+) · 88 of 100: how capable the model is at FP8, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GB10 (128 GB) FP8 · 1.9 min · Slow
88Excellent quality (A+) · 88 of 100: how capable the model is at FP8, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX Spark N1X (6144-core GPU) (128 GB) FP8 · 2.0 min · Slow

Quick answers

How much memory does HunyuanImage 3.0 (80B-A13B) need on the NVIDIA RTX 6000 Ada Generation 48GB?

At FP8 it needs about 32.8 GB. The NVIDIA RTX 6000 Ada Generation 48GB has 48.0 GB usable.

How long does HunyuanImage 3.0 (80B-A13B) take on the NVIDIA RTX 6000 Ada Generation 48GB?

About 65 s per image at FP8 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.