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Can the NVIDIA Jetson AGX Orin 64GB (64 GB) run HunyuanVideo I2V 13B?

71Great quality (A) · 71 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly

Very slow. At Q8 GGUF it needs about 44.6 GB of 57.6 GB usable and takes about 6.0 h per clip.

Best precision
Q8 GGUF
Memory mode
Everything on GPU
Time per clip
6.0 h
Output
1280×720, 129 frames, 50 steps

Best app to run it on the NVIDIA Jetson AGX Orin 64GB (64 GB)

On Linux on ARM boards

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 HunyuanVideo I2V 13B

Recommended for the NVIDIA Jetson AGX Orin 64GB (64 GB): q8_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 q8_gguf (Q8_0) file (opens in a new tab)

huggingface.co/city96/HunyuanVideo-I2V-gguf (opens in a new tab)

Original weights · Hugging Face

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

huggingface.co/tencent/HunyuanVideo-I2V (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/HunyuanVideo-I2V (opens in a new tab)

git clone https://github.com/Tencent-Hunyuan/HunyuanVideo-I2V

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

How it’s rated

Quality 71Great quality (A) · 71 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q8 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 slow
How well it runs on the NVIDIA Jetson AGX Orin 64GB (64 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.32.1 / 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.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.1.4 / 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 score73.4 / 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 Jetson AGX Orin 64GB (64 GB)

Quant Memory needed Available Speed Memory mode Runs Quality Download
FP16 48.6 GB 57.6 GB 5.5 h Text encoder offloaded Very slowencoder unloaded after use 72Great quality (A) · 72 of 100: how capable the model is at FP16, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)
FP8 43.7 GB 57.6 GB 4.7 h Everything on GPU Very slow 70Great quality (A) · 70 of 100: how capable the model is at FP8, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)
Q8 GGUF 44.6 GB 57.6 GB 6.0 h Everything on GPU Very slow 71Great quality (A) · 71 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4 GGUF 38.5 GB 57.6 GB 6.3 h Everything on GPU Very slow 66Good quality (B) · 66 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 71/100 (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 HunyuanVideo I2V 13B need on the NVIDIA Jetson AGX Orin 64GB 64GB?

At Q8 GGUF it needs about 44.6 GB. The NVIDIA Jetson AGX Orin 64GB 64GB has 57.6 GB usable.

How long does HunyuanVideo I2V 13B take on the NVIDIA Jetson AGX Orin 64GB 64GB?

About 6.0 h per clip at Q8 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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