We are excited to present a big milestone. A comprehensive selection of GPU deployment options from data centers worldwide is now available on the Fluence GPU platform. Users can now deploy their workloads not only in containers, but also on virtual machines and bare metal instances, and select a model that best meets their performance and control security requirements — all via a single protocol and API.
From now on Fluence offers three deployment models that cover most GPU use cases:
- GPU Containers — for fast, standardized deployments
Deploy lightweight, containerized GPU workloads in a managed environment with minimal configuration overhead. Containers are ideal for production inference, quick experiments, CI/CD pipelines, and any standardized applications where fast startup and easy scaling matter most.
- GPU VMs — for full control over the environment
GPU virtual machines are perfect when users need complete control over the computing environment and maximum flexibility in the deployments configuration. Virtual Servers are a great fit for complex workloads with persistent state, orchestrating multiple containers or services on the same node, and scenarios that require specific OS versions, kernel modules, or system-level software.
- GPU Bare Metal — for maximum, predictable performance
Bare-metal instances are a go when direct access to hardware is crucial: no hypervisor layer, no shared virtualization stack, just the full raw performance potential of the server. This option gives the most predictable performance and the strongest hardware-level isolation, at the cost of more operational responsibility. It is well-suited for latency-sensitive applications and large-scale model training where every percent of throughput and efficiency matters.Users can rent instances with one or more GPUs and advanced networking options, such as NVLink.
Thanks to the current protocol catalogue and platform capabilities, anyone can now use Fluence to run everything from LLM inference and fine-tuning to complex, distributed AI model training. All this is available at a price lower than that of major providers, yet on the basiы of enterprise-grade infrastructure and high-tier data centers.
