Get bare metal performance, GPU firepower, and ultra-low latency with RedSwitches AI dedicated server solutions. Perfect for scaling artificial intelligence fast.
2 x Intel Xeon 5318Y
CPU
48 Cores/96 Threads - 2,1 GHz
GPU
T4
2 x Intel Xeon E5-2620v4
CPU
16 Cores/32 Threads - 2,1 GHz
GPU
T4
2 x Intel Xeon E5-2620v4
CPU
16 Cores/32 Threads - 2,1 GHz
GPU
T4
2 x Intel Xeon 5218
CPU
32 Cores/64 Threads - 2,3 GHz
GPU
T4
2 x Intel Xeon 4214
CPU
24 Cores/48 Threads - 2,2 GHz
GPU
T4
2 x AMD EPYC 7413
CPU
48 Cores/96 Threads - 2,65 GHz
GPU
L4
2 x Intel Xeon E5-2630v4
CPU
20 Cores/40 Threads - 2,2 GHz
GPU
T4
2 x Intel Xeon 5118
CPU
24 Cores/48 Threads - 2,2 GHz
GPU
T4
2 x Intel Xeon 5118
CPU
24 Cores/48 Threads - 2,2 GHz
GPU
T4
2 x Intel Xeon 6134
CPU
16 Cores/32 Threads - 3,2 GHz
GPU
T4
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| Model | Mem (Type) | Mem BW (GB/s) |
Power (W) |
FP64 (TFLOPS) |
FP32 (TFLOPS) |
INT8 (TOPS) |
Use-Case |
|---|---|---|---|---|---|---|---|
| NVIDIA L40S | 48 GB GDDR6 | 864 | 350 | N/A | 91.6 | 733 | Inference & Graphics |
| NVIDIA H100 | 80 GB HBM3 | 3 350 | 700 | N/A | ≈ 60 | 1 216 | LLM Training, HPC |
| NVIDIA Tesla T4 | 16 GB GDDR6 | 300 | 70 | N/A | 8.1 | 130 | Edge Inference, Transcode |
| NVIDIA L4 | 24 GB GDDR6 | 300 | 72 | N/A | 30 | 147 | Video AI, Inference |
| NVIDIA H200 | 141 GB HBM3e | 4 800 | 700 | N/A | N/A | N/A | Next-Gen LLM & HPC |
| NVIDIA A30 | 24 GB HBM2 | 933 | 165 | N/A | 10.3 | 330 | AI Training, HPC |
| AMD MI210 | 64 GB HBM2e | 1 600 | 300 | 11.9 | 22.7 | N/A | FP64 HPC, AI Training |
| NVIDIA A100 | 40 GB HBM2e | 1 555 | 250 | 9.7 | 19.5 | 624 | Training & Analytics |
| NVIDIA RTX A4000 | 16 GB GDDR6 (with ECC) | 448 | 140 | N/A | ≈ 19.2 | N/A | Professional Graphics, AI-accelerated Compute |
| NVIDIA RTX A5000 | 24 GB GDDR6 (with ECC) | 768 | 230 | N/A | ≈ 27.8 | ≈ 444 (Int8) | High-End Graphics, AI & Compute Tasks |
| NVIDIA Tesla V100 | 16 GB (or 32 GB) HBM2 | 900 | 300 | 7–7.8 | 15–15.7 | TFLOPS ≈ 125 | AI Training, HPC |
AI-Ready Infrastructure
RedSwitches AI dedicated servers are architected from the ground up to support artificial intelligence workloads. From high-throughput data movement to compute orchestration, every layer is designed to meet the demands of model training, fine-tuning, and inference at scale.
Accelerated Model Training
Our infrastructure significantly reduces training time for complex AI models. With high-bandwidth memory paths, intelligent job scheduling, and fast data access, users can iterate more quickly, optimize more frequently, and achieve convergence with greater efficiency.
Inference Optimization
RedSwitches servers are tuned for low-latency inference workloads, supporting real-time AI applications such as NLP, computer vision, and recommendation systems. Dedicated resources ensure consistent response times under production-scale loads.
Liquid Cooling Ready
Our artificial intelligence servers use advanced liquid cooling to maintain thermal stability. This is essential when running multi-GPU setups or H100 clusters under full load. Liquid cooling prevents thermal throttling, increases efficiency, and prolongs hardware lifespan for AI workloads.
Bare Metal Isolation
RedSwitches provides full bare metal access with no virtualization overhead. This means all CPU, GPU, RAM, and I/O resources are 100% yours. Ideal for latency-sensitive and compute-intensive servers for AI that demand guaranteed performance and full customization.
Framework-Agnostic Environments
All servers come preconfigured to support leading AI frameworks, including TensorFlow, PyTorch, JAX, and ONNX. Environments are container-ready and driver-optimized to eliminate compatibility issues and streamline deployment across different workloads.
End-to-End Pipeline Support
Our systems are optimized to run the entire AI lifecycle, from data ingestion and preprocessing to model deployment and monitoring. This enables seamless integration with CI/CD pipelines, MLOps tools, and automated retraining processes.
Reduced Iteration Cycles
By minimizing resource contention and optimizing workload-specific performance parameters, our infrastructure helps reduce iteration times across various AI models, accelerating the build, train, test, and deploy cycle.
Support for Advanced Architectures
From transformer-based models to diffusion networks and hybrid LLM frameworks, our systems support the memory, compute, and storage demands of next-generation AI architectures without compromise on throughput or reliability.
Global Availability Zones
With strategically located data centers worldwide, RedSwitches enables low-latency access to AI workloads. This supports geographically distributed training teams, ensures faster data synchronization, and facilitates regional model deployment for compliance.
Integrated Telemetry and Monitoring
All AI-dedicated servers include system-level telemetry for real-time performance tracking. This includes GPU/CPU utilization, thermal thresholds, memory usage, and I/O behavior, all of which are essential for debugging, tuning, and workload optimization.
24/7 Support
Our sales and technical support teams are available 24/7, with a guaranteed response time of under 5 minutes. Whether you're configuring complex AI environments or resolving urgent issues, our experts ensure uninterrupted deployment and rapid issue resolution.
Real-Time Inference
Deploy AI models for real-time decision-making with ultra-low latency. Our infrastructure ensures stable inference performance, even under peak loads. Ideal for applications like chatbots, recommendation systems, and fraud detection that require sub-second model execution in production environments.
Robotics and Control
Simulate and train reinforcement learning models for robotic control, path planning, or manipulation. RedSwitches' infrastructure supports iterative training cycles and reward-based tuning, making it ideal for control systems that evolve in high-fidelity simulated or real-world environments.
Natural Language Processing
Run complex NLP pipelines for sentiment analysis, summarization, language translation, or question answering. RedSwitches AI servers provide the compute power and memory isolation required to process large corpora, fine-tune pre-trained models, and deliver scalable language-based AI applications.
Generative AI Projects
Train and deploy generative models for creating text, images, videos, or audio. From LLMs to diffusion and GAN architectures, our servers support fast iteration cycles, model checkpointing, and fine-tuning, critical for GenAI projects focused on originality, control, and fidelity.
MLOps and Automation
Integrate with CI/CD and MLOps tools for automated retraining, deployment, and monitoring. RedSwitches infrastructure supports reproducible builds, model versioning, and containerized environments, making it ideal for teams deploying continuous machine learning pipelines and production-grade AI services.
Scientific Simulations
Run physics-based simulations, molecular modeling, and climate forecasting using parallel AI models. Our AI dedicated servers support large matrix operations and simulation workflows that require sustained compute throughput, high memory capacity, and distributed storage access across time-sensitive research applications.
Healthcare AI Models
Deploy HIPAA-compliant AI workloads for diagnostics, imaging, and patient data analysis. With isolated infrastructure and encryption options, RedSwitches' servers meet strict compliance needs while enabling accurate model training on sensitive medical datasets with zero performance trade-offs.
Edge AI Prototyping
Develop and test AI models for edge deployment on our centralized infrastructure. RedSwitches servers simulate real-world constraints, such as low latency and limited bandwidth, ensuring models are optimized before being deployed in smart devices or edge locations.
Blockchain and Web3
Accelerate smart contract analysis, fraud detection, and decentralized AI applications with high-throughput infrastructure. RedSwitches AI servers process real-time blockchain data, support AI-driven consensus mechanisms, and power Web3 analytics tools requiring fast indexing, model inference, and low-latency execution.
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Frequently Asked Questions
An AI server in 2025 should be purpose-built for high-performance computing tasks, such as model training and inference. At its core, it should feature:
At RedSwitches, our AI servers combine these components to deliver the throughput and responsiveness required for real-time AI, generative models, and multi-modal workloads. This architecture ensures your compute stack is not only fast but scalable and reliable, ready for production today and expansion tomorrow.
In AI servers, CPUs don’t perform the core training computations; that’s the GPU’s job. But CPUs are vital for:
A powerful server-grade CPU, such as AMD EPYC or Intel Xeon, ensures that your AI workloads don’t bottleneck during job scheduling, data loading, or framework-level execution.
AI servers operate as two-part systems:
Think of the CPU as the traffic controller and the GPU as the expressway; both are essential for performance. RedSwitches balances both with carefully matched hardware profiles to ensure neither becomes a bottleneck.
Yes, our AI infrastructure is designed for modular scaling. Start with a single-GPU setup, and expand as your needs evolve. We support:
Whether you’re scaling MLOps workflows, deploying vision transformers, or expanding LLM hosting, RedSwitches ensures your server grows with you. We also provide architectural planning, so you won’t need to migrate; you can simply upgrade. For more information, please contact our expert via live chat now.
Data security is foundational to our AI hosting environments. At RedSwitches, we deploy:
All servers are housed in ISO 27001, GDPR, and HIPAA-compliant data centers. Whether you’re in healthcare, fintech, or enterprise AI, your models and datasets are protected both in transit and at rest, meeting the strictest compliance requirements while maintaining performance integrity.
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