SadTalker GPU Benchmarks:
RTX 4090 vs RTX 3090
We tested SadTalker using identical inputs: a 512×512 corporate presenter portrait paired with an 11.24s studio speech track, --still mode, and the GFPGAN v1.4 face restoration neural network. Here is the verified head-to-head performance breakdown.
Standardized Test Assets & Reproducible Command
Both GPUs executed the exact same input assets and CLI arguments to ensure 100% scientific parity.
time python inference.py --driven_audio ./sadtalker-male-narration-audio.wav --source_image ./sadtalker-corporate-presenter-source.png --enhancer gfpgan --still --size 512
- • Resolution: 512×512
- • Head Motion: --still (Anchored)
- • Enhancer: GFPGAN v1.4
- • Preprocess: crop
Output Video Comparison: RTX 4090 vs RTX 3090
Inspect both full-resolution 512×512 MP4 videos generated by each graphics card. Compare lip sync fidelity, natural eyelid blinks, and GFPGAN face skin pore reconstruction.
SadTalker GPU Performance & Cloud Cost Comparison
Comparative matrix for generating an 11.24s audio clip at 512×512 resolution with GFPGAN enabled across consumer GPUs and data center accelerators.
| GPU Model | Architecture | VRAM | Render Time (11s audio) | Cloud Hourly Rate | Cost / Video | Rating | Best For |
|---|---|---|---|---|---|---|---|
| NVIDIA RTX 5090 | Blackwell (GB202) | 32 GB GDDR7 | ~26 seconds | ~$0.75 / hr | $0.0054 | ⭐⭐⭐⭐⭐ | Next-Gen Enthusiast |
| NVIDIA RTX 4090Verified | Ada Lovelace (AD102) | 24 GB GDDR6X | 40.49 seconds | ~$0.47 / hr | $0.0053 | ⭐⭐⭐⭐⭐ | Overall Fastest Tested |
| NVIDIA RTX 3090Verified | Ampere (GA102) | 24 GB GDDR6X | 2m 10.13s (130s) | ~$0.22 / hr | $0.0079 | ⭐⭐⭐⭐⭐ | Best Value 24GB VRAM |
| NVIDIA RTX 3060 | Ampere (GA106) | 12 GB GDDR6 | ~3m 15s (195s) | ~$0.15 / hr | $0.0081 | ⭐⭐⭐⭐ | Budget Local Desktop |
| Tesla T4 (Google Colab) | Turing (TU104) | 15 GB GDDR6 | 2m 47s (167s) | Free / $0.00 | $0.0000 | ⭐⭐⭐⭐ | Free Cloud Access |
| NVIDIA A100 SXM4 | Ampere (GA100) | 40 GB / 80 GB HBM2e | 31 seconds | ~$1.20 / hr | $0.0103 | ⭐⭐⭐⭐⭐ | Enterprise Workstation |
| NVIDIA H100 SXM5 | Hopper (GH100) | 80 GB HBM3 | 18 seconds | ~$2.80 / hr | $0.0140 | ⭐⭐⭐⭐⭐ | Hyperscale Research Lab |
Why is RTX 4090 3.2x Faster than RTX 3090 with Same 24GB VRAM?
On paper, both GPUs feature 24GB of GDDR6X VRAM and 384-bit memory buses. Yet in SadTalker, the 4090 finishes in 40.49s compared to 130.13s on the 3090. Here is what happens under the hood:
GFPGAN face restoration repeatedly loops through super-resolution feature maps across 281 frames. The 3090’s small 6MB cache constantly spills into external memory. The 4090’s 72MB cache keeps the entire frame weights resident in on-chip SRAM.
The Ada Lovelace architecture runs at a ~48% higher core clock frequency on TSMC 4N process node compared to the Samsung 8nm node used in Ampere, greatly accelerating single-stream audio-to-expression calculations.
The 4th Gen Tensor Cores and FP8 tensor operations offer more than double the mathematical throughput for PyTorch convolutional layers, delivering 10.60 it/s on GFPGAN versus 2.75 it/s on 3rd Gen Ampere cores.
NVIDIA GeForce RTX 3090: Telemetry & nvtop Screen Run
Comprehensive performance audit, real terminal timing logs, and full nvtop recording.
RTX 3090 Terminal Execution Telemetry
RTX 3090 Verdict for SadTalker
Terminal execution on RTX 3090: real 2m10.135s | user 2m26.223s | sys 0m3.235s
SadTalker Inference Time & Generation Time on NVIDIA RTX 4090
Detailed stage-by-stage latency analysis measuring real SadTalker generation time on RTX 4090 (40.490s total run time, 10.60 it/s GFPGAN inference speed).
RTX 4090 Stage-by-Stage Latency
RTX 4090 Verdict for SadTalker
NVIDIA GeForce RTX 3060 (12GB): Performance & Hardware Requirements
Empirical SadTalker generation times, inference speeds, and hardware requirements for the NVIDIA GeForce RTX 3060 12GB.
SadTalker Hardware Requirements for RTX 3060
The NVIDIA GeForce RTX 3060 12GB is considered the ideal entry-level graphics card for local SadTalker installations. Because SadTalker’s GFPGAN super-resolution pass consumes a peak of 8.2 GB to 8.3 GB of VRAM during 512×512 synthesis, standard 8GB graphics cards (like RTX 3070 8GB or RTX 4060 8GB) frequently trigger CUDA Out Of Memory errors unless restricted. The RTX 3060’s 12GB frame buffer completely bypasses this limitation.
- No VRAM Spills: Holds 3DMM coeff, face render tensors, and GFPGAN weights in VRAM simultaneously.
- Affordable Power Draw: 170W TDP allows execution with a standard 550W power supply.
- Full CUDA 12 & FP16 Support: 3,584 Ampere CUDA cores with 2nd gen RT and 3rd gen Tensor cores.
set PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:128RTX 3060 vs RTX 3090 vs RTX 4090 Latency
Experience Interactive NVIDIA RTX Demos
Curious about the real-world output quality before installing? Our interactive SadTalker Demo Studio lets you test pre-rendered audio-driven talking head avatars rendered directly on NVIDIA RTX hardware (RTX 4090 and RTX 3090) with synchronised 512px GFPGAN face restoration.
Need the Installation Commands for Your GPU?
The setup commands, Conda environment, PyTorch wheels, and model weights are 100% identical for RTX 3060, RTX 3090, RTX 4090, RTX 5090, and cloud instances. Check out our comprehensive step-by-step installation manual: