New: GB300 Benchmarks Available
GPUs
The Engine
Raw compute power. Necessary, but often underutilized in standard setups leading to wasted spend.
Tokens
The Work Unit
The actual output you sell. Optimizing specifically for token throughput changes the economics entirely.
Result
True Cost
Cost per token is your true AI operating cost. We minimize this metric above all else.
VM Options
GB300-START
1x GPU Unit
1x GPU Unit
24GB VRAM
Standard Networking
GB300-PRO
1x GPU Unit
4x GPU Cluster
96GB High-Bandwidth VRAM
Zero-Latency Interconnect
GB300-SUPERNODE
1x GPU Unit
8x+ Custom Cluster
Multi-TB Shared VRAM
Dedicated Fiber Line
Tokens / $
Delivers significantly higher throughput per dollar than legacy GPU clouds by optimizing for inference, not just raw FLOPs.
Lower Latency
High-speed interconnects and memory bandwidth reduce time-to-first-token, even under high concurrency loads.
Uptime SLA
Dense "Supernode" architecture reduces rack complexity and failure points, ensuring enterprise-grade stability.
If your infrastructure delivers 2–3× more tokens per dollar, your AI margin improves immediately.
Most legacy GPU clouds were built for training, not inference. The GB300 architecture cuts the fat, optimizing purely for the metric that matters: throughput per dollar spent.
Any more questions?
Is GB300 more expensive than standard H100s?
No. While the raw hourly rate for a fully clustered node might look comparable, the efficiency gain means your cost-per-token drops by 40-60%. You get more throughput for the same spend.
Do we need to rewrite our entire stack?
Absolutely not. GB300 instances are fully compatible with standard container orchestration tools (Kubernetes, Docker) and popular inference servers (vLLM, TGI).
What’s the migration risk?
We offer a zero-downtime migration pilot. You can run GB300 in parallel with your current setup for 14 days at no cost to validate performance before switching traffic.