A GPU is a chip that can do many small math tasks at the same time. Firstly, it was made for graphics, but now it’s a key part of AI. In 2026, GPUs were used by most AI workloads for speed and efficiency.
Choosing the right GPU is important. The best GPU for training is not always the best GPU for inference. Here’s Viperatech to help you find the right fit based on models, data, and budget.
An enterprise GPU is a high-performance GPU designed for servers and data centers. It’s different from a consuming or gaming GPU.
It Offers:
24/7 operation
Large memory for big models
Thermal efficiency and cooling
Long-term driver and software support
Virtualization support and multi-instance GPU
These GPUs are installed in workstations, rack servers, or multi-GPU AI servers. Businesses, research labs, and cloud providers use them.
Viperatech offers a range of enterprise GPU models from leading vendors. With different memory sizes and performance levels, they are created for both training and inference workloads.
AI has two phases: training and inference.
Training is when the model learns.
Large Datasets
Foundation for inference
High computer power and time
Inference is when the trainerd model makes predictions.
Smaller computational requirements
Low Latency
User-focused and good power efficiency
In simple terms:
Training is like teaching someone a new skill, which takes long sessions and deep focus.
Inference is like using that skill every day; that’s faster, but it happens very often.
More VRAM lets you:
Use larger batch size
Train bigger models
Avoid out-of-memory errors
We need to look for:
High VRAM capacity
High-speed memory, such as HBM, if your budget allows
Check:
Tensor performance
Number of CUDA cores or similar compute units
Mixed-precision support
When you train large models across multiple GPUs, you need:
Fast links between GPUs
Good support in your framework
If you want large-scale training, connect Viperatech for multi-node designs, such as supermicro GPU server platforms optimised for multi-GPU fabrics.
You may not need the most expensive enterprise GPU for inference. Instead you want:
Enough performance to meet response time targets
Ability to serve many requests in parallel.
Good Scaling across multiple GPUs if needed
Check:
Model size in gigabytes
Any extra memory needed for batching
For many common models, a mid-range GPU for inference with moderate VRAM is enough.
When comapring GPUs, consider:
Performance per watt
Data center power limits
Cooling needs
Viperatech can help you compare different options and estimate ongoing power costs for your inference cluster.
While choosing an enterprise GPU, not only does raw speed matters but also:
Can it handle your largest model and batch?
Is there room for future growth?
Can your circuits and racks support the GPUs?
Is enough cooling there for peak load?
Hardware price
Power and cooling over the years
Maintenance and support
Viperatech is a trusted partner for AI hardware. We understand that each team has unique needs:
Research groups running complex experiments
Enterprises deploying AI into core products
Startups testing new models
We:
Help you compare on-prem, hosted and, hybrid setups.
Provide enterprise GPUs, AI servers, and AI processors in one place
Listen to your workload requirements
Recommend GPU options for training and inference
For a broad overview of how GPUs, servers, and processors fit together, read our pillar guide: AI Hardware Guide: GPUs, Servers, and How to Pick the Right One.
Feel free to visit Viperatech’s website to know about our real GPU options or contact our team for a recommendation based on your workload.
