Inference vs Training Server
The right server solution for your AI applications
Understand the differences between AI inference and training workloads and find the optimal server configuration for your requirements.
Train or Deploy AI Models?
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.
What is a Training Server?
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.
Training Hardware Requirements
High VRAM
48 GB+ for large models and batch processing
Maximum Compute Power
TFLOPS and Tensor Cores for faster training runs
System Memory
128 GB+ RAM for large datasets
Fast Storage
NVMe SSD for data access during training
Training Server Use Cases
- Developing and training new AI models from scratch
- Fine-tuning existing models with your own data
- Hyperparameter optimization and model experiments
- Transfer learning with large foundation models
- Research and development of new architectures
What is an Inference Server?
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.
Inference Hardware Requirements
Moderate VRAM
20-24 GB sufficient for most models
Low Latency
Fast response times for end users
High Throughput
Process many parallel requests simultaneously
Model Optimization
Quantization and pruning for efficiency
Inference Server Use Cases
- Production deployment of chatbots and AI assistants
- API endpoints for predictions in applications
- Real-time analysis and classification
- Content generation and text processing
- Automation and intelligent workflows
Direct Comparison: Training vs. Inference
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 Ada (48 GB) | RTX 4000 Ada (20 GB) |
VRAM Requirements | 48 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 | €1,399.90 | €499.90 |
Scaling | Vertical (more power) | Horizontal (more instances) |
Workload Type | Batch processing | Request/Response |
Optimization Goal | Training speed | Latency & throughput |
Main Purpose
Develop & train models
Deploy models in production
GPU Recommendation
RTX 6000 Ada (48 GB)
RTX 4000 Ada (20 GB)
VRAM Requirements
48 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
€1,399.90
€499.90
Scaling
Vertical (more power)
Horizontal (more instances)
Workload Type
Batch processing
Request/Response
Optimization Goal
Training speed
Latency & throughput
Our Server Solutions Overview
The Right Hardware for Every Use Case
AI Server Basic
Perfect for inference and production deployments
- Optimized for inference workloads
- Low latency for real-time applications
- 20 GB VRAM for medium-sized models
- Perfect for production APIs
- Cost-effective in operation
AI Server Pro
For training and large models
- Maximum computing power for training
- 48 GB VRAM for large models
- Fine-tuning and hyperparameter optimization
- Also suitable for large inference models
- Development and research
Hybrid Approach Possible
Combine training and inference servers for optimal workflows: train on the Pro server and deploy on cost-effective Basic servers for production.
Decision Guide: Which Server is Right for Me?
Answer These Questions for the Right Choice
Do you want to develop your own models?
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.
How large are your models?
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.
How is your budget structured?
During development, you need maximum flexibility and power for experiments.
In production, cost efficiency with consistent performance matters.
What are your latency requirements?
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.
Typical Workflows
Startup / MVP
Start with an inference server and existing models. Fast time-to-market, low costs.
Growth
Scale horizontally with multiple inference servers for higher capacity and fault tolerance.
Enterprise
Combine training servers for development with multiple inference servers for production. Optimal price-performance ratio.
Research & Development
Training server for model development and experiments. Optional inference servers for demos and testing.
Further Considerations
Data Sovereignty
Both server types offer full control over your data. Server location Germany, GDPR compliant.
Managed Service
Upon request, we take care of installation, configuration, and maintenance – for both training and inference (optional).
Easy Migration
Start with one server type and switch if needed. Models are portable.
Expert Support
Our team helps you select and optimize your server configuration.
Ready for Your AI Infrastructure?
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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What do our customers say?
Sonja Aßer
Data Manager, ARGE
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