RTX 5090 vs 4090 in the real world of image-to-video inference.
Real-time video generation is surging—from meme automation to high-scale creative workflows—and with it comes a critical question: which GPU actually delivers when inference load ramps up? This blog post is the first in a new series born out of real customer requests and internal curiosity. We benchmarked the RTX 4090 and the new 5090 using WAN 2.1, a state-of-the-art image-to-video (i2v) model, not just to get faster numbers, but to help AI builders, cloud platforms, and media teams understand how these GPUs perform in practice—not in theory. Whether you’re chasing every second of runtime or trying to balance performance with power draw, this post gives you the data and context to make a smarter call. And yes, we plan to do more.
Benchmark Setup
- Model: WAN 2.1 i2v (Image-to-Video)
- Runs: 100+ per GPU
- Tracked: Execution time, VRAM/RAM peak, power draw (min/avg/peak)
It's worth noting, to run these benchmarks, we partially followed the setup outlined in the ComfyUI WAN 2.1 i2v video model guide, adapting it to suit our GPU environment and inference pipeline. This provided a solid foundation for consistent testing while allowing us to tailor performance tracking and logging for our specific use case.
The Raw Numbers
Power consumption comparison
The power profile of the RTX 5090 paints a clear picture of raw, unrestrained performance, but at a significant energy cost. Its average power draw more than doubles that of the RTX 4090, peaking at nearly 587 watts and idling no lower than 85 watts during active use. In contrast, the 4090 maintains a far leaner footprint, averaging just 235 watts and dipping to a whisper-quiet 14 watts when under minimal load. This stark contrast highlights the 5090’s role as a high-throughput workhorse, while the 4090 remains a more efficient choice for workloads that prioritize thermal headroom, power budgets, or multi-GPU scaling.
Speed vs. Efficiency
The RTX 5090 is nearly 45% faster, slashing runtime from ~12.7 minutes to just 7 minutes. But speed comes at a steep price of more than double the average power draw, and it guzzles a persistent 85 W of power even at low activity. In contrast, the 4090 virtually idles at 14 W when not working hard.
VRAM & RAM: Where the two differ
The RTX 5090 consumes considerably more VRAM than the 4090, likely due to larger model buffers or internal optimizations. With usage exceeding 24 GB during inference, it may pose challenges for systems with limited memory capacity or GPUs shared across workloads.
Efficiency Snapshot
RTX 5090
A high-performance GPU built for peak output and fast turnaround. The RTX 5090 excels at image-to-video generation when raw speed is non-negotiable and workloads demand maximum throughput per unit of time. While it draws considerably more power than its predecessor, that trade-off pays dividends in scenarios where time-to-result is the highest priority.
Best for:
- Time-sensitive inference where every second matters—ideal for live pipelines or fast-paced iteration
- Batch-heavy jobs that benefit from reduced overall processing time, especially in shorter bursts
- Isolated high-output environments where power usage is less of a constraint and rapid delivery is the main goal
RTX 4090
A more power-efficient option that still delivers excellent performance for image-to-video generation, the RTX 4090 shines in environments where balance is key. With lower average and idle power draw, it enables smarter scaling and better cost-efficiency, especially in multi-GPU setups or sustained inference tasks.
Best for:
- Multi-GPU deployments where energy efficiency directly affects scalability and cooling requirements
- Long-running jobs that prioritize stability, predictable performance, and reduced total power cost
- Cost-sensitive operations that still require strong inference performance without over-provisioning power or thermal overhead
Scaling Across the Cloud
Whether you’re spinning up a GPU on-prem or on-demand, these differences matter. Cloud platforms that integrate real-time access to top-tier GPUs offer flexibility, letting you optimize for peak performance, cost-efficiency, or energy footprint, depending on your workload.
Platforms that allow per-second billing, no contracts, and transparent pricing mean you can:
- Choose the 5090 for time-sensitive, demanding inference.
- Fall back to the 4090 for sustained, large-scale jobs where energy per inference matters.
- Dynamically switch based on performance needs—without over-provisioning.
The Bottom Line
- RTX 5090: This is definitely the faster card and is much better if every second counts.
- RTX 4090: This is notably more efficient, and is the smarter choice for budget-conscious or multi-tenant models.
And when your cloud system supports on-demand access to both, with transparent pricing and no lock-ins, you get the best of both worlds: scale, performance, and efficiency on your terms.
Final Takeaway
Your workload defines your gear, and the cloud platform defines your flexibility. If your application needs real-time inferencing, the RTX 5090 is the clear frontrunner. But if you’re optimizing cost, energy, or multi-node efficiency, the 4090 is still a powerhouse.
Having both available dynamically with smart provisioning lets you dial in for performance when it matters and scale responsibly when it doesn’t.