HunyuanVideo Pro
Open-source foundation video generation model featuring a dual-stream Diffusion Transformer (DiT) architecture capable of generating cinematic 1080p 60fps video clips with physically accurate lighting and complex camera motion.
HunyuanVideo Pro
Open-source foundation video generation model featuring a dual-stream Diffusion Transformer (DiT) architecture capable of generating cinematic 1080p 60fps video clips with physically accurate lighting and complex camera motion.
- • FP16 Weights = 13.0B × 2B = 26.00 GB
- • INT4 Weights = 13.0B × 0.55B = 7.15 GB
- • KV Cache (16384 ctx, FP16) ≈ 2.00 GB
- • Activation Buffer = ~20% overhead
Hardware & Execution ParametersVideo
Genealogical Graph & Evolutionary Provenance
Tracing foundational base architecture ancestry, architectural successors, scale siblings, and reasoning distillation derivatives.
HunyuanVideo Pro operates as an autonomous foundation model architecture without direct precursor derivatives in this catalog.
Architecture Engineering & Capability Deep-Dive
An objective architectural evaluation of HunyuanVideo Pro by Tencent AI Lab, analyzing underlying compute dynamics, memory constraints, and deployment economics.
Topology & Attention Mechanics
A robust autoregressive transformer utilizing standard attention patterns for predictable and coherent token generation.
Evaluation Profile & Reasoning
Exhibits frontier-tier behavior in reasoning and coding.
LLM Hardware Sizing & Serving
For open deployments via vLLM/SGLang, quantization (INT4/AWQ) is heavily recommended to fit dense memory constraints, or multi-GPU pipeline parallelism for full FP16.
Inference Economics & Workflows
Well-suited for enterprise pipelines where capability is balanced against per-million token costs.
Architectural Strengths vs. Considerations
An objective balance sheet analyzing the operational advantages and production constraints of deploying HunyuanVideo Pro.
Key Architectural Strengths
- Ultra cost-effective inference at $0/1M input tokens enables high-frequency agent loops.
- Demonstrated Dynamic Motion Score evaluation score of 88.1% in verified benchmarks.
Operational Considerations
- Non-deterministic reasoning chains require schema validation in safety-critical deployments.
Inference Runtimes & Hardware Sizing
Deployment targets, inference engines, and memory requirements for HunyuanVideo Pro.
python3 -m vllm.entrypoints.openai.api_server --model HunyuanVideo Pro --tensor-parallel-size 1 --gpu-memory-utilization 0.95 --max-model-len 4096 --enable-chunked-prefillollama run hunyuanvideo propython3 -m sglang.launch_server --model-path HunyuanVideo Pro --tp 1 --trust-remote-codetext-generation-launcher --model-id HunyuanVideo Pro --num-shard 1 --max-batch-prefill-tokens 32000High-throughput PagedAttention server
One-click CLI & local desktop serving
Fast multi-turn structured decoding
Text Generation Inference
GGUF CPU/Apple Silicon execution
~31.2 GB VRAM required
~15.6 GB VRAM (Hopper speedup)
~8.6 GB VRAM
CPU RAM / Apple Silicon optimized
LLM Benchmark Database & Performance Metrics
4 TestedStandardized evaluation results across reasoning, agentic coding, computer use, and alignment.
Dynamic Motion Score
VBench Video Quality
Text Alignment Score
Physical Consistency Score
API & Deployment Pricing
Open-weights model available for local and private cloud deployment. Compute costs depend on the target GPU hardware instance.
| Usage Tier | Rate / Unit |
|---|---|
| Prompt / Input Tokens | $0 / 1M tokens |
| Completion / Output Tokens | $0 / 1M tokens |
Comparable Foundation Architectures
Alternative models in the Video class with similar capabilities, context windows, or deployment profiles.
Qwen3.8 Flash Next
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("TENCENT_AI_LAB_KEY", "EMPTY"),
base_url="http://localhost:8000/v1"
)
response = client.chat.completions.create(
model="hunyuan-video-pro-2026",
messages=[{"role": "user", "content": "Explain quantum superposition in 2 sentences."}]
)
print(response.choices[0].message.content)Frequently Asked Questions about HunyuanVideo Pro
Essential facts, architectural specs, hardware constraints, and pricing answers for HunyuanVideo Pro.
To run HunyuanVideo Pro (13B) locally, you generally need Depends on quantization. We recommend using quantized GGUF/AWQ formats with Ollama or vLLM to optimize memory footprint.
All technical specifications, parameter distributions, context architectures, and benchmark evaluations for HunyuanVideo Pro are audited against primary source release documentation, research whitepapers, and verified vendor API endpoints.