Dedicated GPU power for real AI workloads
Fiberax deploys cloud GPU services for training, inference, data science, rendering, and simulation, with predictable resources, enterprise-grade protection, and a clear path from ready-to-run L4 instances to larger AI infrastructure.
Both plans include 24 CPU cores, 160 GB RAM, 1600 GB of disk, 500 Mbps network capability, and optional backups.
Fiberax provides cloud GPU services for teams that need dedicated acceleration, high-memory virtual machines, and a deployment model that is clear before procurement starts.
You can choose GPU L4 Linux at €650 net per month and GPU L4 Windows at €722 net per month. Both plans include 24 CPU cores, 160 GB RAM, 1600 GB of disk, a 500 Mbps network capability, and optional backups. This makes it easier to size a GPU cloud server for inference, preprocessing, graphics, and data-heavy applications because the starting resource profile is explicit.
In the current Linux flow, we provide Ubuntu 24.04 LTS GPU L4 templates, including an image with the NVIDIA driver already added. That saves setup time for teams that want to start training, deploy containers, run CUDA-based applications, or validate a model without doing manual driver preparation first. In practical terms, this turns GPU cloud computing into an operational task rather than a hardware project.
Additional VM storage on CephFS is available, so storage can grow with model checkpoints, generated assets, logs, and processing outputs. That matters when workloads move from proof of concept to recurring production jobs.
Our cloud GPU service supports backups with 7 stored copies, and the backup scope can include both the base VM and additional CephFS storage. This makes recovery planning cleaner, and gives teams a more predictable restore model for business-critical data than a self-managed bare-metal solution.
Our cloud-based GPU farms run on four compute nodes powered by powerful AMD processors, DDR5 memory, and mirrored NVMe storage in RAID 1. Faster storage and modern CPU capacity improve data movement, job stability, and overall platform responsiveness, which is important when AI, rendering, or simulation workloads stay under load for long sessions.
We use higher-tier GPU options such as H200 NVL when the workload involves large language models, long context windows, or dense RAG (retrieval-augmented generation) pipelines. Fast inter-GPU communication and high memory bandwidth reduce bottlenecks during model training, tuning, and latency-sensitive inference.
NVIDIA L4 is positioned for lightweight RAG, high-fanout APIs, and lower-power inference services that scale horizontally. This is a strong fit for customer support bots, internal assistants, document Q&A systems, and other workloads where response consistency matters more than maximum model size.
Our cloud GPU instances fit detectors, trackers, captioning pipelines, video analytics, and frame-by-frame processing jobs. L4 is a good match for streaming computer vision and real-time captioning, which makes the platform practical for surveillance analytics, media workflows, manufacturing inspection, and event-based data processing.
L40S and RTX Pro 6000 Blackwell extend the service beyond pure machine learning. These options are relevant when one environment must support AI inference, multimodal applications, graphics acceleration, and rendering-oriented tasks in the same delivery model. It is a practical fit for studios, visualization teams, and product groups building image-heavy applications.
Rendering and simulation are targeted workloads for our cloud GPU rental services. That makes the platform suitable for 3D scene output, design review, synthetic data generation, and compute-heavy graphics processing when local workstations would become a scheduling bottleneck.
Many projects need cloud GPU resources before a model is ever deployed. Teams use this service for dataset preparation, vector preprocessing, batch transformation, experiment runs, and analytics pipelines that benefit from parallel processing. A ready server with 24 CPU cores, 160 GB RAM, and dedicated NVIDIA acceleration provides enough headroom for serious data handling.
When a standard instance is not enough, you can rent GPU cloud multi-node servers with AMD EPYC 4584PX, DDR5, NVMe RAID 1, and L4. This is relevant for teams that need stricter isolation, repeatable deployment architecture, or more controlled infrastructure for longer-running deep learning and compute workloads.
A managed GPU platform in the cloud is useful not only for researchers. Engineering teams can use it for model views, staging, containerized services, batch jobs, benchmarking, and production release workflows. Because the service is already integrated with backup options, additional CephFS storage, and centralized control, it supports both experiments and operational delivery.
A Cloud GPU is an optimized virtual machine or instance with a dedicated graphics processor for parallel computing. At Fiberax, a cloud GPU server combines NVIDIA L4 acceleration with defined CPU, RAM, storage, and network resources, so teams can run AI inference, model training, rendering, or video processing without buying physical hardware first.
The main advantages are faster launch, predictable monthly planning, and easier scaling. Fiberax provides a GPU cloud service with fixed L4 plans, optional daily backups, extra CephFS storage, and an Ubuntu 24.04 LTS image with the NVIDIA driver already included. That reduces setup work, shortens time to access, and makes the environment easier to standardize across teams and applications.
Workloads that rely on parallel math usually need GPUs. Typical cases include LLM inference, fine-tuning, computer vision, video analytics, rendering, simulation, vector preprocessing, and high-throughput APIs. H200 NVL is positioned for large-model training, L40S and RTX Pro 6000 Blackwell for mixed AI and graphics tasks, and L4 is suitable for efficient inference and streaming CV workloads.
Cloud GPU is usually stronger when fast deployment and variable demand matter most. Dedicated hardware can make more sense for constant full-load use or custom topology, but cloud removes procurement delays and idle capital. We run the platform on AMD EPYC 9654 compute nodes with DDR5 and mirrored NVMe RAID 1, which supports strong baseline performance for production computing and compute-heavy services.
The main cost drivers are GPU model, operating system, CPU and RAM allocation, storage, backup, and billing model selection. On Fiberax, current L4 plans differ by Linux or Windows, and pricing also changes when additional CephFS capacity or daily backups are added. For buyers comparing a cloud GPU rent model with owned hardware, utilization time and protection scope usually shape the monthly total the most.
We include security at the platform level. Fiberax offers advanced encryption, 24/7 monitoring, and compliance with global standards. We protect our service environment with access control systems, CCTV, redundant power, fire protection, environmental monitoring, and ISO-aligned operational practices. For regulated workloads, security is built into the operating environment from launch.
Fiberax has Linux and Windows GPU options. The Linux configuration lists Ubuntu 24.04 LTS and Ubuntu 24.04 LTS with the NVIDIA driver already included, while the catalog also includes a Windows-based L4 plan. Framework support is environment-driven: teams install the AI, computing, or graphic applications they require on top of the selected image and driver stack.
Launch your GPU server in minutes — Linux or Windows, NVIDIA driver included.