AI Runs on GPUs: Here’s What You Need to Know
In today’s AI landscape, performance isn’t just about better algorithms—it’s about better hardware. GPUs (Graphics Processing Units) have become the core of modern AI infrastructure, powering everything from image generation to training massive language models.
But if you're not a developer or cloud engineer, how do you make use of all that power? That’s where Nebula Block comes in.
Why GPUs Matter for AI
If you've used AI tools for writing, coding, or image generation, you’ve already benefitted from GPU acceleration. But what makes GPUs different from CPUs?
CPUs (Central Processing Units) are like Swiss Army knives — good at many tasks, but not built for heavy-duty parallel computing. GPUs, on the other hand, are like multi-lane highways. They’re optimized to handle thousands of computations simultaneously — perfect for deep learning, model inference, and LLM fine-tuning.
Without GPUs, training today’s large models would take weeks or months. With them? Hours or even minutes.
What Makes GPUs Perfect for AI?
- Massive Parallelism: GPUs execute thousands of operations at once—ideal for training large models.
- Deep Learning Acceleration: Tensor Cores and matrix math enable efficient neural network training and inference.
- High Throughput: Measured in TFLOPS, this determines how fast your models can train.
- Memory Capacity (VRAM): More memory means you can run bigger models and process larger datasets.
- Scalability: Need more power? GPUs can be clustered for even heavier compute tasks.
At Nebula Block, you can choose from a range of GPU types—RTX 4090, A100, H100, and more—based on your workload and budget.
How Nebula Block Makes It Easy
Not everyone wants to set up containers, manage drivers, or deal with cloud scaling headaches. That’s why Nebula Block offers:
- Serverless GPUs: Run inference workloads without managing infrastructure.
- Instant Clusters: Launch distributed GPU environments in seconds.
- Pre-built runtimes: Support for popular frameworks like PyTorch, Jupyter, and Hugging Face.
- Flexible GPU Options: Choose from RTX 4090, A100, H100, and more.
- No DevOps Required: Just plug into the Nebula dashboard, CLI, or API and start building.
- Data Privacy and Security: User data is safeguarded through encrypted transfers and fully isolated environments, ensuring both security and regulatory compliance.
Serverless or Cluster? Use the Right Tool for the Job
Use Case | Best Fit | Why |
|---|---|---|
Real-time inference or APIs | Serverless GPU | Fast, simple, pay-per-use |
Training or fine-tuning models | GPU Cluster | High memory, distributed compute |
Prototyping or demos | Single GPU Pod | Quick startup, cost-effective |
No Code? No Problem
Nebula Block is built for builders of all backgrounds. You can:
- Launch prebuilt models with one click.
- Run inference via API using serverless endpoints.
- Deploy containers using Docker or web UI.
- Monitor usage and scale automatically.
Whether you're a researcher, startup, or product team, Nebula Block gives you the infrastructure to move fast without managing it.
Which GPU Should You Choose?
At Nebula Block, you can pick the GPU that matches your workload.
RTX 4090 – Ideal for prototyping, image generation, or small-scale LLMs. Great balance of price and performance.
A100 – Optimized for training larger models with high VRAM needs and stable throughput.
H100/H200 – Best for enterprise-grade workloads, fine-tuning massive models, or ultra-fast inference.
Not sure where to begin? Start with the 4090 and scale up only when you need to.
AI Builders Welcome
Whether you're a founder using AI to power your product, a marketer using LLMs to generate content, or a researcher exploring multimodal workflows — understanding how infrastructure works helps you make smarter decisions.
And with platforms like Nebula Block, you don’t need to be a DevOps expert to take advantage of enterprise-grade GPU performance.
Next Steps
Sign up and experience.
Visit our blog for more insights or schedule a demo to optimize your search solutions.
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