The right server solution for your AI applications
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When choosing the right server infrastructure for artificial intelligence, the distinction between training and inference is crucial.
While training AI models requires enormous computing resources over long periods of time, inference – i.e. the practical use of trained models – requires above all fast response times and efficient throughput.
The right decision can save significant costs while optimizing the performance of your AI applications.
Powerful Hardware for Model Development
A training server is designed for the computationally intensive task of machine learning training. Here, neural networks are fed with large amounts of data in order to recognize and learn patterns.
The training process can take days to weeks and requires maximum computing power to optimize model parameters.
48 GB+ for large models and batch processing
TFLOPS and Tensor Cores for faster training runs
128 GB+ RAM for large datasets
NVMe SSD for data access during training
Optimized for Fast Production Deployments
An inference server uses pre-trained models to deliver predictions and results in real time. The focus here is on speed and efficiency.
Inference requires significantly fewer resources than training, as only forward passes through the network are computed – without backpropagation or weight updates.
20-24 GB sufficient for most models
Fast response times for end users
Process many parallel requests simultaneously
Quantization and pruning for efficiency
The Most Important Differences at a Glance
| Aspect | Training Server | Inference Server |
|---|---|---|
Main Purpose | Develop & train models | Deploy models in production |
GPU Recommendation | RTX 6000 Blackwell Max-Q (96 GB) | RTX 4000 Ada (20 GB) |
VRAM Requirements | 96 GB for large models | 20-24 GB sufficient |
Computing Power | 1457 TFLOPS (Maximum) | 307 TFLOPS (Optimal) |
Time Characteristics | Hours to weeks | Milliseconds to seconds |
Monthly Cost | on request | €499.90 |
Scaling | Vertical (more power) | Horizontal (more instances) |
Workload Type | Batch processing | Request/Response |
Optimization Goal | Training speed | Latency & throughput |
Develop & train models
Deploy models in production
RTX 6000 Blackwell Max-Q (96 GB)
RTX 4000 Ada (20 GB)
96 GB for large models
20-24 GB sufficient
1457 TFLOPS (Maximum)
307 TFLOPS (Optimal)
Hours to weeks
Milliseconds to seconds
on request
€499.90
Vertical (more power)
Horizontal (more instances)
Batch processing
Request/Response
Training speed
Latency & throughput
The Right Hardware for Every Use Case
Perfect for inference and production deployments
For training and large models
Combine training and inference servers for optimal workflows: train on the Pro server and deploy on cost-effective Basic servers for production.
Answer These Questions for the Right Choice
You need maximum computing power and lots of VRAM for training new models or fine-tuning.
You use existing, pre-trained models for production applications and APIs.
Models like Llama 3.1 70B or larger require 48 GB+ VRAM, even for inference.
Most production models like Gemma 27B, DeepSeek 32B run perfectly on 20 GB.
During development, you need maximum flexibility and power for experiments.
In production, cost efficiency with consistent performance matters.
For APIs, chatbots, and interactive applications, an optimized inference server is ideal.
For non-time-critical analyses, you can leverage the power of the training server.
Start with an inference server and existing models. Fast time-to-market, low costs.
Scale horizontally with multiple inference servers for higher capacity and fault tolerance.
Combine training servers for development with multiple inference servers for production. Optimal price-performance ratio.
Training server for model development and experiments. Optional inference servers for demos and testing.
Both server types offer full control over your data. Server location Germany, GDPR compliant.
Upon request, we take care of installation, configuration, and maintenance – for both training and inference (optional).
Start with one server type and switch if needed. Models are portable.
Our team helps you select and optimize your server configuration.
Let's find the optimal server solution for your project together
Unsure which server fits your needs? Book a free consultation with our CTO and find the best solution for your AI requirements.
Or contact us directly
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