Built to Build AI

Most CSPs rely on virtualization solutions (such as KVM or equivalent) that subject workloads to a virtualization tax, which adds significant overhead, limits compute efficiency, and increases time to solution. There’s a reason no supercomputer uses virtualization in its software stack. Radium’s platform solves significant technical challenges by removing hypervisors from both network and compute layers, providing the most FLOPs of any on-demand cloud provider — essentially creating supercomputers on-demand. Training workloads are 50% faster while inference loads accelerate by 135% compared to traditional hyperscalers.

Leading LLM inference platforms generally run on third-party infrastructure, eliminating their ability to optimize across software and hardware. Further, these platforms are beholden to third-party compliance and costly integrations. Radium is built from the infra layer up, allowing for significant architectural advantages that translate into lower token rates (measured by $/M tokens) than our peers.

We’ve automated all time-consuming resource configuration processes within the Gen AI journey, from connecting data stores and repositories, partitioning data to projects and their related networks, to deploying LLMs, including optimization of all drivers, libraries, and compilers to the project network.

Our automation of computing and networking lets you start/stop, archive, and recall models to be trained with no long-term multi-year infrastructure commitments. Our vLAN automation lets you deploy, redeploy and scale infrastructure instantly. Go from a single node to multi-node supercomputers or running nodes in parallel.

Move from high-performance training to high-performance inference in the same environment. Deploy models within your company or externally through API endpoints quickly and easily. Get access to leading open-source LLMs like Llama2, LLaVa, Mistral, Mixtral, DBRX, Groc, Falcon and more, as well as proprietary models like Nebula and Mimic, reducing development time and cost. Have your own model? Deploying your organization’s custom model is extremely simple.

Network-level switch automation is unique to Radium. This feature allows for parallelization and scaling to multi-node or even multi-cluster, multi-region deployments without degrading throughput, even across thousands of GPUs. This is a key differentiator compared to other platforms where performance tends to degrade as jobs are scaled up.

Radium’s full-stack architecture ensures your data remains secure in private, unshared networks, all the way down to the switch level, which provides secure computing with hardware attestation. This is in contrast to incumbents who rely on virtualization and open source platform architecture that is prone to data security risks.

For developers working with regulated data and sensitive workloads, our custom-built codebase and infrastructure simplifies compliance with regulatory requirements to protect and secure your AI.