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AI, ML & Big Data Servers

Harness the raw computational power of dedicated GPU servers for your most demanding AI, Machine Learning, and Big Data Analytics workloads.

Get maximum performance, 100% resource dedication, and total control over your data—all with no egress fees or unpredictable cloud billing. Power your innovation with iRexta.

Abstract visualization of an AI neural network processing data

Why Choose a Dedicated Server for AI & Big Data?

The Challenge: Powering Data-Intensive Workloads

AI, Machine Learning, and Big Data Analytics are not like standard applications. They require sustained, high-intensity processing of massive datasets. Shared cloud environments often fall short due to resource contention, unpredictable "noisy neighbor" effects, and staggering data transfer (egress) fees.

AI & Machine Learning Workloads

Training a model (like Deep Learning or NLP) involves trillions of parallel computations. This is a job for specialized hardware, primarily Graphics Processing Units (GPUs).

  • Requires massive parallel processing (GPU).
  • Needs ultra-fast storage (NVMe) for dataset access.
  • Intolerant to latency or performance dips.

Big Data Analytics Workloads

Processing terabytes of data (e.g., with Apache Spark or Hadoop) relies on high CPU core counts, massive amounts of RAM, and fast I/O to read and write data.

  • Requires high CPU core counts (e.g., 32, 64, or 128 cores).
  • Needs enormous amounts of RAM (256GB, 512GB, or 1TB+).
  • Demands high-throughput storage for data warehousing.

The Solution: The Dedicated Server Advantage

A dedicated server from iRexta provides the raw, unshared power your data-intensive applications demand. You get 100% of the resources, 100% of the time.

Unmatched GPU Power

Get dedicated access to elite NVIDIA GPUs like the A100, H100, and RTX A6000 for maximum training performance.

Total Data Sovereignty

Keep your sensitive, proprietary data on a private, secure physical server. You control the data, the security, and the access.

Massive Scalability

Scale up with high-core CPUs (Intel Xeon, AMD EPYC), terabytes of RAM, and petabytes of NVMe storage.

Zero Egress Fees

Move your data in and out as much as you need. With our unmetered bandwidth, you'll never pay unpredictable data transfer fees again.

How to Choose the Best Server for Your Workload

The right configuration depends on your specific goal. A server built for Deep Learning looks very different from one built for Data Warehousing.

For Machine Learning (ML)

This is all about massive parallel processing. Your priority is the GPU for training. The CPU manages the data pipeline, but the GPU does the heavy lifting.

Recommended Hardware:
  • GPU: 1x to 4x NVIDIA H100, A100, or RTX 6000 Ada
  • CPU: 1x or 2x High-Frequency CPU (e.g., Intel Xeon W-series or AMD Ryzen Threadripper PRO)
  • RAM: 256GB - 1.5TB+ DDR5 ECC (or DDR4 ECC depending on the CPU platform)
  • Storage: 2x 4TB High-Speed NVMe SSD (Gen 4 or Gen 5) for datasets & OS

For Artificial Intelligence (AI)

"AI" often involves a mix of training (GPU) and inference (CPU/GPU). This requires a balanced server with strong CPUs for logic and fast GPUs for parallel tasks.

Recommended Hardware:
  • GPU: 1x or 2x NVIDIA L40S, L4, or RTX 5000 Ada (Balanced for mixed training/inference)
  • CPU: 1x or 2x High-Core CPU (e.g., Intel Xeon Scalable or AMD EPYC 16-32 Cores)
  • RAM: 256GB - 1TB+ DDR5 ECC
  • Storage: 2x 4TB NVMe SSD (Gen 4) for OS & active models

For Big Data Analytics

This workload is often GPU-less. It's a brute-force data-crunching task that relies on CPU cores and high-capacity RAM. You need to fit your entire dataset in memory for high-speed processing.

Recommended Hardware:
  • GPU: 1x to 4x NVIDIA L40S or NVIDIA H100 (for ML/AI-heavy analytics)
  • CPU: 1x or 2x High-Core-Count CPU (e.g., AMD EPYC or Intel Xeon Scalable)
  • RAM: 512GB - 4TB+ DDR5 ECC
  • Storage: 8x to 24x High-Capacity U.2 NVMe SSD (Gen 4/5) in a high-speed RAID configuration

Popular Use Cases

Our servers are the engine behind innovation for researchers, startups, and enterprise.

Deep Learning

Natural Language (NLP)

Computer Vision

Predictive Analytics

Frequently Asked Questions

Cloud providers are great for small, burstable workloads. But for sustained AI/ML training or large-scale data analytics, you pay a massive premium. More importantly, they charge exorbitant "egress fees" to move your data. With a dedicated server, you get 100% of the performance for a flat monthly price and zero data transfer fees.

Data Center GPUs (like the A100 or H100) have features like high-bandwidth memory (HBM), larger VRAM (80GB+), and are built for 24/7/365 reliability and high-precision computing (FP64). Consumer GPUs (like the RTX 4090 or RTX A6000) offer incredible performance for their price (FP32) and are a fantastic, cost-effective choice for many research and development tasks.

For GPU workloads (ML/AI), a good rule of thumb is to have at least 2x the amount of system RAM as you have total GPU VRAM. For example, if you have two NVIDIA A100 80GB cards (160GB VRAM), you should have at least 256GB of system RAM.
For Big Data workloads, you need enough RAM to fit your entire "hot" dataset in memory. This is why configurations often start at 512GB and go to 2TB or more.

Absolutely. The configurations on our site are just the beginning. If you need a specific build with 4x, 8x, or 10x GPUs, specialized storage, or multi-node clustering, please contact our solutions team. We build custom-spec servers for enterprise and research clients every day.