Modern AI is system-limited, not model-limited
Radium removes the abstraction layers that slow every other cloud down. At the kernel, orchestration, and network level. So you get closer to peak hardware performance than anywhere else.
latency vs AWS
Every other cloud adds layers. We take them away
Hyperscalers and neo clouds bolt inference on top of a virtualization stack built for general compute. That means your GPU is always fighting with a hypervisor, an overlay network, and a scheduler that has no idea what an AI workload even is.
Radium was built from the ground up with AI workloads as the primary design constraint.
Inference API
Intelligent Orchestration
(Optimized OS)
Network Fabric
Physical Infrastructure
The cost of abstraction compounds with model complexity.
As models get deeper and workloads get heavier, every layer of generalization you're running on top of becomes more expensive. That's why our performance advantage grows with model size — not shrinks.
Radium owns every interface between layers — that's where performance is won or lost.
Built on the best open-source models. Delivered through infrastructure only Radium can provide
Each Radium model is powered by leading open-source foundations, optimized through our proprietary orchestration, kernel, and network stack to deliver up to [X]% lower cost at equivalent quality. We select and update base models continuously to ensure you always run the best available architecture.
Three levels of optimization.
One integrated stack.
Performance isn't won at any single layer. Radium optimizes across all three simultaneously — and they compound.
Intelligent
Orchestration
Most schedulers treat everything as generic batch workloads. Radium's RL-based orchestration engine knows the difference between training, fine-tuning, and inference — and optimizes for each one's distinct performance profile.
Workload-aware scheduling — not
just "first available GPU"
Topology-aware placement across
NUMA, SXM, PCIe, and fabric
Auto-scaling that stays network- and
latency-aware
Closed-loop control using live system
telemetry
Kernel-Level
Optimization
The Linux kernel is designed to be fair to all workloads. That's exactly the wrong design for AI. Radium's kernel is tuned to bias toward locality, predictability, and throughput for GPU workloads — not fairness.
CPU affinity and thread pinning
aligned to NUMA topology
Interrupt routing to minimize cross-
socket latency
Large memory allocation local to the
GPU consuming it
Deterministic scheduling to eliminate
jitter spikes
Network-Level
Optimization
Radium treats networking as a first-class component — not a layer managed by someone else's CNI plugin. Custom routing logic, purpose-built DNS/DHCP, and a dual-fabric architecture separating AI traffic from control/storage.
InfiniBand for AI training traffic,
Ethernet for control/storage
Custom software on switches —
topology and congestion-aware routing
Proprietary DNS and DHCP for
deterministic startup/teardown
Multi-datacenter fabric bridging
without performance cliff
Third-party validated. Real workloads, not microbenchmarks.
latency vs AWS
Training & Inference
Under Mixed Load
CMU ran a variational autoencoder on concurrent CPU+GPU workloads.
On AWS, interrupt and scheduling noise caused latency spikes as virtualization layers compounded.
On Radium: stable, fast, predictable. Same hardware class, comparable price point.
identical JAX|
workloads vs GCP
Model FLOP
Utilization vs GCP
Stanford's Center for Research on Foundation Models ran GPT2-XL training on the same software stack, same hardware generation, across both clouds.
Radium consistently reached 50–55% MFU. GCP plateaued at 39–45%.
Radium also outperformed Stanford's own on-premises HPC cluster.
16-layer depth vs
AWS
Performance
Advantage Scales
With Model Depth
At 4 layers: 30% advantage. At 16 layers: 58% advantage. The performance gap compounds as models get deeper — more kernel launches, more memory movement, more synchronization.
Radium's optimizations handle exactly these operations better than a generic virtualized environment.
Built different.
Not just deployed different.
The gap isn't configuration — it's architecture. Here's what changes when you build a cloud around AI from day one.
The same workloads, at a lower cost to operate
Radium models align with familiar performance tiers, but are delivered through infrastructure designed to reduce the cost of running them in production.
Radium
Open Source
Hyperscalers
One line of code to switch.
A different class of performance.
Swap OpenAI for Radium in your API call. That's it.