Placement
Coordinates workload placement across clusters, clouds, and regions, making better use of available capacity while respecting latency, locality, and resource constraints.
Global allocationMore workloads. Same fleet.
Tandemn unlocks GPU capacity stranded across deployments, hardware types, clouds, and regions. Its open-source central planner coordinates the entire fleet, continuously learning from serving performance to make smarter workload configuration and placement decisions.
By treating fragmented infrastructure as a single pool of capacity, Tandemn enables teams to run more inference workloads on the GPUs they already have while meeting serving performance targets and reducing cost-per-token.
Your stack, coordinated.
Tandemn adds an intelligent planning layer to your existing GPU infrastructure. It works with your orchestration and serving systems to coordinate workloads across clusters, hardware types, clouds, and regions—without replacing the tools you already use.
Everything runs within your environment, keeping your models, workload data, and telemetry under your control.
- Kubernetes
- NVIDIA Dynamo
- Ray
- vLLM
- SGLang
- Custom system
One fleet. One integrated planner.
Tandemn is a single integrated planner that treats your entire GPU infrastructure as a single pool of capacity, regardless of hardware type, cluster, cloud, or region. Rather than making placement, configuration, and resource allocation decisions independently, it evaluates them together—accounting for how each decision affects other workloads and the fleet as a whole.
The result is a coordinated, fleet-wide plan that makes more capacity available for inference workloads while meeting their performance requirements.
Six interconnected capabilities make this possible:
Hardware matching
Matches workloads to suitable GPU types and memory capacities, preserving scarce accelerators for workloads that need them.
Hardware fitConfiguration
Evaluates serving engines, parallelism, quantization, and runtime settings to find configurations that meet performance targets with fewer resources.
Runtime decisionsAutoscaling
Adjusts replica counts as demand changes, recovering excess capacity and making it available to other workloads.
Capacity recoveryWorkload requirements
Balances performance targets, deadlines, priorities, and quotas when workloads compete for shared GPU resources.
Shared resourcesEvolution
Compares predicted performance with serving telemetry and updates the planner’s evidence. Refines configurations and placements as models, demand, and infrastructure change.
Continuous learningDynamic workloads. Adaptive infrastructure.
What makes Tandemn different is its ability to continuously learn, predict and adapt as workloads change. As traffic, models, and request patterns shift, Tandemn updates its fleet-wide plan to reflect changing conditions, releasing capacity where it’s no longer needed and making it available to other workloads.
Tandemn gets smarter with experience. By learning from actual serving performance and the interactions among hundreds of variables, it continuously sharpens its predictions and improves its decisions—making the entire fleet more efficient over time.
Frequently asked questions
What does Tandemn do?
Tandemn is an open-source, evolving planner for inference fleets. It coordinates model configurations, GPU selection, replica counts, and placement across heterogeneous hardware, clouds, and regions so enterprises can admit more workloads on their existing infrastructure.
Where does Tandemn fit in our stack?
Tandemn sits above your orchestration and serving systems as the central planning layer. The Tandemn execution engine translates plans into actions through your existing infrastructure, including NVIDIA Dynamo, Ray, Kubernetes, and custom systems. Your serving frameworks and operational tools stay in place. Explore deployment
How does the planning algorithm work?
The algorithm observes workloads, available resources, and serving telemetry, then proposes configurations and placements for the whole fleet. Deterministic tools predict performance, score candidates, and check constraints. Observed deployment results refine the evidence used in the next planning cycle. Explore the algorithm
What makes the planner different?
Tandemn optimizes configuration and placement together across the whole fleet. It accounts for how one job’s allocation affects others and learns from actual serving behavior. Language models propose candidates using causal evidence; deterministic tools predict outcomes, enforce constraints, and select a feasible plan.
What does 2.4× effective capacity mean?
It means admitting 2.4 times the workload on the same GPU fleet while meeting serving targets. For an enterprise, that creates room for more models, more online demand, and more batch jobs. Tandemn unlocks capacity stranded across fragmented and overprovisioned deployments, with latency, cost, deadlines, and priorities guiding the plan.