The latest NVIDIA GTC 2025 keynote reminded us once again how fast the world of accelerated computing keeps moving. From the announcement of Blackwell Ultra to the unveiling of the next-generation Rubin platform, NVIDIA’s message is clear: Compute power will continue to scale at an unprecedented pace. As NVIDIA CEO Jensen Huang said, "Accelerated computing has reached the tipping point: General-purpose is no longer enough. The future is accelerated." (Watch again his keynote here).
This is an exciting time for our industry. Rubin, expected to launch in 2026, promises to double the performance of Blackwell GPUs, which themselves are only now being deployed at scale. However, for AI teams, cloud providers, and decision-makers, the real question isn’t what’s next? It’s what’s best for my workload, today?
While the pace of innovation is thrilling, most AI teams are still busy deploying what was announced two years ago. Hopper-based GPUs such as the NVIDIA H100 and H200 are now becoming widely available after the first contract waves expired, reshaping the landscape of GPU supply.
The good news is that many of the workloads that drive real business value today, like fine-tuning, inference, and retrieval-augmented generation (RAG), are perfectly suited to proven GPU generations like NVIDIA A100, NVIDIA H100, or NVIDIA H200.
Prices are adjusting, availability is growing, and these GPUs continue to deliver excellent performance and reliability, often without the premium cost of the newest models.
Industry benchmarks back this up. According to MLCommons' latest Training results (v4.1, November 2024), Hopper-based NVIDIA H100 GPUs achieved excellent throughput and efficiency across key AI models like BERT and ResNet. These are not just benchmark favorites, they are the types of models many AI teams run daily to power their products.
The same results show that while newer GPUs, such as the NVIDIA H200, offer incremental improvements in memory capacity and bandwidth, the actual performance uplift heavily depends on the workload. For many production use cases, such as fine-tuning a foundation model, running real-time inference, or scaling POCs NVIDIA H100 and A100 GPUs remain a highly cost-efficient choice.
This means you can optimize your infrastructure today and accelerate your projects with hardware that’s battle-tested, available, and efficient.
At Genesis Cloud, we believe smart workloads need smart infrastructure. Our mission is to help you identify and access the right GPU resources for your specific needs: No overprovisioning, no unnecessary spend.
We work closely with AI teams to:
In the coming weeks, we’ll also be introducing thousands of new GPUs into our platform, including NVIDIA B200, H100, H200, A100, and other particularly cost-efficient GPU types. Explore our current GPU availability, pricing and data center locations, or sign up and accelerate here!
Choosing the right GPU for your workload isn’t about chasing the latest release: It’s about understanding what your model and use case actually require. Here’s how many teams approach their selection:
That said, let’s not overlook that beyond data center GPUs, there are also highly efficient and affordable options like the NVIDIA RTX 3090 and similar models. For many AI teams and researchers, these GPUs offer an excellent cost-performance ratio for smaller-scale experiments, early-stage model development, or inference workloads. They continue to play an important role in making AI compute accessible to a broader community.
As NVIDIA continues to innovate, we will update our offering with close attention to our customers’ needs and help make technology accessible to a broader range of users. But our job is not just to follow the curve of technology. It's to help you ride it in the smartest, most effective way possible.
In a world of rapid change and shifting hardware cycles, Genesis Cloud is here to help you get the most out of every generation: Maximizing performance, minimizing cost, and accelerating time to value.
Have questions about which GPU is right for your workload? Let’s talk.
The Genesis Cloud team 🚀
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