We see a distinct market gap for highly available, on-demand 8-node B200 clusters based entirely within the European Union. While hyperscalers focus on generic global expansion, the local European infrastructure base remains heavily constrained for teams that require immediate hardware access.

You hit this exact friction the moment you try to scale your AI operations locally, discovering that there is simply no single player you can go to to buy that specific capacity today. If you need eight B200 nodes on demand within an EU base right now, the few existing infrastructure providers either lack the availability to actually fill the order or they operate at price points that consume your entire training budget. At the same time, major US providers continue to allocate engineering bandwidth toward vertical workflow tools for the entertainment industry rather than scaling core hardware availability, as demonstrated by CoreWeave expanding its Conductor software to power creative pipelines in Lights. Camera. Inference.

We resolve this structural supply bottleneck directly through Lyceum VMs & Infrastructure. You get raw root SSH access to provisioned instances scaling from 1 to 8 GPUs, including NVLink configurations, available in 18 seconds across GDPR-compliant European data centers with per-second billing and zero minimum commitment.

Building infrastructure for raw compute differs fundamentally from building for model serving. The architecture behind Lyceum’s Inference Engine thrives on a highly distributed network to ensure high availability and efficient request routing. In contrast, provisioning high-quality hardware for VMs & Infrastructure requires strictly collocated nodes, ideally connected via Infiniband, to handle intensive workloads without latency bottlenecks.

This hardware reality shapes how your team should scale. When you need to quickly prototype, Serverless Execution is the starting point. You submit your Python script, and Lyceum auto-containerizes it, minimizing cold starts and stopping the per-second billing the moment your training job completes.

When transitioning to serving, the distributed architecture of the Inference Engine takes over. You can deploy any Hugging Face model to a dedicated, EU-sovereign GPU that scales to zero when idle. Because the engine is fully OpenAI SDK compatible, routing your existing application to your own infrastructure requires zero code changes, just a simple base URL swap:

As you push toward heavy production or require absolute environment control, you graduate to VMs & Infrastructure. This is where those strictly collocated, Infiniband-connected nodes matter most, granting you full root SSH access and NVLink configurations provisioned across European data centers in 18 seconds.

This modular, open-stack platform is not the right fit if your workflow relies entirely on managed, black-box proprietary APIs and your team has no desire to control or optimize the underlying compute.

When discussing the predictable and often painful compute transitions that development teams face as they scale their artificial intelligence workloads, an engineering founder we recently spoke with summarized this inevitable infrastructure migration perfectly.

Rather than forcing developers to endure the extreme operational friction of tearing down and completely rebuilding their underlying architecture at every single stage of commercial maturity, Lyceum provides a unified orchestration and compute platform that bridges the gap from rapid serverless experimentation to massive dedicated enterprise deployments without requiring costly code rewrites.