Develop on your cpu only workstation
Run CUDA kernels on your centralized pool of NVIDIA or AMD GPUs
- Zero friction: IDEs & notebooks work as-is.
- Higher utilization vs. idle, dedicated GPUs.
- Burst to any available GPU in the pool.
A single CUDA container that runs on NVIDIA+AMD
Our Runtime Just-In-Time compiles to native GPU ISA at execution time
- Simpler CI/CD and fewer base images.
- Freedom to buy whichever GPUs are available/cheaper.
- One image across on-prem & clouds.
VRAM Dedup & Multi-Adapter Concurrency to maximize memory efficiency
Shared base model with many independent LoRA adapter stacks on every GPU
- Base weights shared in VRAM; adapters isolated.
- Higher throughput for eval/dev at the same cost.
- Per-workload priority policies protect latency and fairness.
True GPU concurrency: dynamic cores + VRAM, no time slices
Higher density and consistent performance
More workloads per GPU
Priority-aware fairness per workload
Predictable latency with no noisy neighbor impact