We built VRAM AI because
GPU waste was invisible
Every AI team we talked to had the same problem: they were running 8 models across 4 GPUs, most of them idle, with no visibility into which model was costing them money at 3am.
VRAM AI is our answer to that. VRAM AI handles the model lifecycle. X-Ray makes the waste visible. Together they give your team full control over what runs on your GPU — and what doesn't.
Make every GPU cycle count
The AI industry is burning billions of dollars on idle GPU time. Models loaded into VRAM and forgotten. Inference servers sleeping between requests. Hardware sitting at 4% utilization while the billing meter runs.
VRAM AI exists to fix that at the infrastructure layer — so AI teams can focus on building products, not managing memory.
What we believe
GPU efficiency first
Every design decision starts with one question: does this help teams waste less GPU compute? Hardware is expensive. Software should make it count.
Production honesty
We build for real inference workloads — not benchmarks. That means handling OOM gracefully, swap races, multi-tenant access, and node failures.
No magic, just engineering
Model swapping is an old idea. We just built it properly: NVMe spill, GDS, LRU eviction, prefetch, and LoRA switching — all tested under production load.
Operator-first
X-Ray exists because GPU waste is invisible until it shows up on your bill. We surface it in real-time so your team can act — not discover it two weeks later.
The team
Builders with deep roots in GPU infrastructure and AI systems.