Video models · Tencent

HunyuanVideo 1.5 (8.3B)

Name: HunyuanVideo 1.5 (8.3B)
Author: Tencent

Backbone: 8.3B
Native output: 1280×720, 121 frames
Default steps: 50
License: tencent-hunyuan-community
Released: 2025-11

How to run it

Original weights · Hugging Face

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

huggingface.co/tencent/HunyuanVideo-1.5 (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-1.5 (opens in a new tab)

git clone https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5

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

Apps that run HunyuanVideo 1.5 (8.3B)

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or device in the Model Finder for an exact answer.

Mac (Apple silicon)

Draw Things

Native Mac app with its own fast Metal engine and quantized downloads.

Also good:

Windows PC, NVIDIA GPU

ComfyUI

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

Also good:

Linux, NVIDIA or AMD GPU

ComfyUI

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

Also good:

App	Type	Opens it as	Systems	Engine	Start
ComfyUI	Desktop app	Built-in support	macOSWindowsLinux	PyTorch, GGUF via ComfyUI-GGUF	Get ComfyUI
Draw Things	Desktop app	Built-in support	macOSiOS	Draw Things	Open in Draw Things
SwarmUI	Desktop app	Built-in support	macOSWindowsLinux	ComfyUI	Get SwarmUI
SD.Next	Desktop app	Built-in support	macOSWindowsLinux	PyTorch	Get SD.Next
Stability Matrix	Desktop app	Built-in support	macOSWindowsLinux	installs ComfyUI, Forge, InvokeAI, SwarmUI, SD.Next or Fooocus	Get Stability Matrix
WanGP	Desktop app	Built-in support	WindowsLinux	PyTorch	Get WanGP

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory needed

Peak GPU memory at the native output size. Offloading moves the text encoder, or the whole pipeline, to system RAM between steps.

Precision	Everything on GPU	Text encoder offloaded	Sequential offload	Download
`FP16`	55.2 GB	38.2 GB	26.8 GB	Original weights (opens in a new tab)
`FP8`	38.4 GB	29.9 GB	23.9 GB	Original weights (opens in a new tab)

Components

Backbone: 8.3B
VAE: 0.3B
Qwen2.5-VL 7B: 8.29B
ByT5: 0.2B

Best hardware for HunyuanVideo 1.5 (8.3B)

The least expensive GPU in our catalog that runs it without offloading is the Intel Arc Pro B70 (launch price $949) at FP8.

Quality	Hardware	Quant	Memory	Speed	Runs
84Great quality (A) · 84 of 100: how capable the model is at FP16, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA RTX PRO 6000 Blackwell	`FP16`	55.2 GB / 96.0 GB	22.0 min	Slow
82Great quality (A) · 82 of 100: how capable the model is at FP8, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 5090	`FP8`	29.9 GB / 32.0 GB	23.7 min	Slowtight fit · encoder in RAM
84Great quality (A) · 84 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 H100 80GB SXM	`FP16`	55.2 GB / 80.0 GB	20.9 min	Slow
84Great quality (A) · 84 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 H800 80GB SXM	`FP16`	55.2 GB / 80.0 GB	20.9 min	Slow
84Great quality (A) · 84 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.	NVIDIA H100 80GB PCIe	`FP16`	55.2 GB / 80.0 GB	25.5 min	Slow
84Great quality (A) · 84 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.	NVIDIA A100 80GB PCIe	`FP16`	55.2 GB / 80.0 GB	31.3 min	Slow
84Great quality (A) · 84 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.	NVIDIA A100 80GB SXM	`FP16`	55.2 GB / 80.0 GB	31.3 min	Slow
84Great quality (A) · 84 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.	NVIDIA A800 80GB	`FP16`	55.2 GB / 80.0 GB	31.3 min	Slow
82Great quality (A) · 82 of 100: how capable the model is at FP8, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA L40S	`FP8`	38.4 GB / 48.0 GB	25.0 min	Slow
82Great quality (A) · 82 of 100: how capable the model is at FP8, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA RTX 6000 Ada Generation	`FP8`	38.4 GB / 48.0 GB	25.1 min	Slow

Check it on your own hardware

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.