Training and inference should not need separate GPU islands.
Use one prioritized resource pool for both. Protect the capacity that must remain available and let eligible spare capacity absorb live inference demand.
Separate pools
- Training poolactiveheadroom
- Inference poolactiveheadroom
- Agents / evaluationactiveheadroom
Each workload gets its own pool. Three pools, three sets of unused headroom.
Prioritized shared pool
One physical resource pool
Protected capacity
Guaranteed workloads remain available
Flexible capacity
- training
- inference
- agents
Allocation changes with demand + policy
Guaranteed work stays protected. Everything else moves with demand and policy, sharing one headroom.
Reuse headroom instead of duplicating it.
The Static Partition Problem
Separate pools mean duplicated headroom.
Training clusters, inference endpoints, and agent evaluation each carry their own peak headroom. When they cannot share capacity, the physical fleet must size for three separate peaks instead of one.
Priority-Aware Convergence
One pool, governed by your priorities.
Nucleaton™ places training and inference into a single resource pool with policies you define. Guaranteed capacity is protected. Eligible spare capacity absorbs inference demand. When high-priority training arrives, supplemental serving yields.
Capacity policy
Protected
Guaranteed capacity. Opportunistic workloads cannot consume it.
Flexible
Eligible workloads use available capacity according to demand and priority.
- training
- inference
- agents
Reclaimable
Return immediately, or stop new requests and drain active inference first.
Live Workload Behavior
Inference is not ordinary batch backfill.
Live inference demand arrives as individual requests, not queued batch jobs. Nucleaton™’s inference endpoints scale based on real request rate and queue depth. When capacity must return to training, the system can reclaim immediately or stop new routing and drain active requests before releasing GPUs.
Live inference can yield capacity safely
Inference scales up
Inference demand
queue / request load
Scale serving
use eligible capacity
Serve requests
baseline + supplemental
Training needs GPUs
higher-priority demand
Choose reclaim behavior
Immediate
terminate supplemental serving
Graceful
stop new routing, drain active requests
Capacity returns
Training receives the GPUs. Supplemental inference can expand again later.
Potential Outcomes
Better economics from shared capacity.
- Use committed training capacity before buying incremental inference capacity.
- Reduce duplicated headroom across workload silos.
- Absorb temporary inference peaks without dedicated serving clusters.
- Run agents and evaluation on eligible spare capacity.
No universal savings percentage. Actual opportunity depends on workload timing, topology, GPU compatibility, model size, and priority constraints.
What determines the opportunity?
The amount of capacity available for sharing depends on your specific workload mix:
- Workload timing and diurnal patterns
- Cluster topology and GPU compatibility
- Model size and parallelism requirements
- Priority constraints and service guarantees
- Inference demand profile
Existing Slurm environments can be integrated through assisted deployment with appropriate administrative access.
FAQs
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