Serve models from the same cluster you train on.
Konduktor schedules training and inference on the same GPUs. Inference scales with traffic, training fills the gaps.
num_nodes: 64
accelerators: H100:8
priority-class: high-priority
serving:
min_replicas: 3
num_nodes: 64
accelerators: H100:8
priority-class: high-priority
serving:
min_replicas: 3
One cluster. Training and inference, always balanced.
Training and inference share one GPU pool. When traffic spikes, the scheduler shifts GPUs to inference; when it drops, they go back to training. Zero traffic means zero GPUs.
Scale to zero. Actually zero.
Not just scale-down-to-one. No requests means no GPUs used. Those resources go straight back to training jobs. When requests come in, they scale back up automatically.
Deploy any model, zero code changes
Open source or your own custom fine-tuned model. Zero code changes required, same workflow as the rest of Konduktor.
Every endpoint monitored, out of the box
Public-facing endpoints are created automatically for every deployed model. Latency, throughput, and GPU utilization are tracked in real time. No extra setup, no third-party dashboards.
Your cluster. Not ours.
On other platforms you rent their infrastructure. Konduktor serves models from your own cluster, so your data never leaves your environment.