Floki NET L4 Single GPU Server, € 758.00/mo. on Linux Dedicated

The L4 Single GPU Server is designed as an efficient entry point into accelerated computing, combining an NVIDIA L4 GPU with balanced CPU, memory and storage resources. Its NVIDIA L4 card comes with 24GB of GDDR6 memory and is rated for around 120 teraFLOPS of tensor performance and 242 teraFLOPS of float16 performance, giving strong throughput for inference services, medium sized training jobs and workloads that benefit from mixed precision compute. The GPU sits alongside an AMD EPYC Grado 4585PX processor with sixteen cores and 64GB of DDR5 RAM, so data preprocessing, feature engineering and orchestration tasks can run on the CPU without starving the accelerator. Local storage is provided by two 400GB NVMe SSDs, delivering high IOPS and low latency for model checkpoints, working datasets and container images while leaving room for log files and application binaries. A 1Gbit network port with a 100Mbps committed bandwidth level is attached, giving reliable throughput for feeding data into the node, exposing APIs or dashboards and synchronising results back to other environments. The configuration is a strong fit for teams that want a dedicated GPU box for continuous inference, fine tuning language models, image generation, video processing or running development and staging workloads before scaling up to larger clusters.

FlokiNET GPU servers are dedicated bare metal machines tuned for AI, machine learning and other high performance computing workloads. Each configuration combines enterprise grade AMD EPYC processors with modern NVIDIA data centre GPUs, fast DDR5 memory and NVMe SSD storage so training runs and inference jobs can saturate the hardware without being constrained by disk or memory bottlenecks. The servers attach to the same network backbone as the provider's colocation and hosting platforms, giving access to multiple carriers, low latency routing across Europe and North America and more than 1Tbit of DDoS filtering capacity at the edge. Power for the Iceland based GPU systems comes from renewable sources, and facilities are engineered with redundant cooling and power paths to keep nodes running reliably throughout long training windows. Root access and IPMI out of band management are included, allowing you to install your preferred Linux distribution, tune kernels and GPUs, and perform hardware level tasks such as reboots or remote media mounting without waiting for technicians. The platform is well suited for a wide variety of accelerated workloads including deep learning, classical machine learning, data analytics, rendering, simulation, scientific research and GPU offloading for container based microservices. Storage can be expanded with additional network volumes or external systems when datasets outgrow local disks, and bandwidth upgrades are available for scenarios where you need faster data ingestion or model serving over the network. Servers are billed per month in euros so costs are predictable even for long running experiments, and contracts can normally be adjusted as your compute requirements grow or shrink over time. Technical support is reachable through ticket, phone and chat channels for infrastructure questions, while you retain full responsibility for models, frameworks and application code running on the machines.