Most AI data centers now run heterogeneous GPU fleets, with several GPU generations side by side and often across different sites. Training frameworks assume identical hardware, so the fastest GPUs end up waiting on the slowest. A single failing card can stall a weeks-long run, while underused servers keep drawing power.
kMotion is part of Federator.ai Cortex, ProphetStor’s full-stack AI Ops platform for AI factories. Federator.ai Cortex predicts how every GPU, link, and facility will behave next. kMotion acts on those predictions with live workload migration, placing, rebalancing, and moving training workloads across heterogeneous GPUs so jobs keep running without manual tuning or restarts.
ERIC-Aware Placement: The Right Place for AI Jobs
kMotion’s placement decisions are built on ProphetStor’s ERIC (Efficient Resource Interchange through Computation) Theorem. ERIC treats every placement as a trade-off between local computation and cross-network communication, and finds the lowest-cost option that still meets the job’s throughput, latency, and memory requirements. Before kMotion places or moves a workload, it weighs four costs at every candidate destination:
Computation
GPU generation, memory capacity, and current load, so work lands where it can run at full speed.
Communication
Thermal
Available thermal headroom at the destination, so jobs avoid hot spots and GPUs stay clear of throttling.
Sustainability
Carbon intensity of each site’s power supply, so flexible workloads can shift toward cleaner, more efficient capacity.
kMotion also spreads work across failure domains, so a single fault cannot halt an entire run. Each score combines a map of the site’s hardware with live telemetry refreshed every few seconds, and the weights shift by workload type: a bandwidth-hungry training job and a compute-dense batch job will intentionally land in different places.
How kMotion Works
Continuous Hardware Awareness
Right-Sized Work for Every GPU
Energy-Aware Consolidation
When utilization drops, kMotion consolidates workloads onto the most efficient GPUs and hibernates idle servers. If ERIC finds a lower-cost option in another data center, the job can move there over high-speed links.
Elastic Scaling During Training
GPUs can join or leave a running job during training. kMotion folds new capacity into the job or reshapes it around a lost GPU, then redistributes the model so training continues with minimal interruption.
Predictive Live Migration
Keep Every GPU Generation Productive
GPU investments shouldn’t expire when the next generation ships. With kMotion, A100 and H100 fleets keep contributing to frontier-scale training alongside GB200/ GB300 systems, so each hardware refresh adds capacity instead of stranding it.
Frequently Asked Questions
What is ProphetStor’s kMotion?
kMotion is a Federator.ai Cortex solution for live workload migration across heterogeneous GPUs. It places, rebalances, and live-migrates LLM training workloads across GPU generations and multiple data centers, guided by predictive analytics.
Can one LLM be trained across different GPU generations?
Yes. kMotion assigns each GPU a share of the model and data matched to its speed and memory, so GB300 and GB200 systems can train the same model alongside H100 and A100 GPUs, without the slowest device setting the pace.