Three products.
One GPU stack.
VRAM Gateway handles model serving. X-Ray handles diagnostics. Model Compression shrinks your models so they need less GPU to run. Together they cut your GPU bill by up to 80%.
Serve unlimited models on a single GPU
VRAM sits in front of your inference stack as an OpenAI-compatible proxy. It maintains a pool of models, swapping them in and out of VRAM based on actual request traffic — not manual management.
You define a VRAM budget. VRAM handles everything else: LRU eviction, NVMe spill, prefetching, LoRA switching, and GPU Direct Storage. Most AI teams run one model per GPU — paying full price for hardware that sits idle between requests. VRAM changes that. By dynamically loading and evicting models based on real traffic, you can run 8–12 models on a single GPU that previously handled one. That's the same throughput at a fraction of the infrastructure cost — cut your GPU spend by up to 60%, serve more models without adding hardware, and plug it in with zero changes to your existing code.
Cut Your GPU CostsKnow exactly what your GPU is doing
X-Ray is a real-time GPU waste scanner embedded directly into VRAM. It surfaces the exact models wasting money, the dollar cost per hour, and prescriptive fixes for each waste pattern.
Accessible at /xray inside the VRAM gateway. No separate deployment needed.
Production-ready from day one
Docker + Helm. Deploys anywhere NVIDIA GPUs run.
Ready to get started?
Talk to our team to get a license key and onboarding support.