Unlock GPU Capacity

Tandemn gets more work out of the GPUs you already run. Its cluster-wide planner allocates capacity where it has the most value; in one benchmark, that meant 45% more throughput at 31% lower cost per token.

Do More With Less Hardware

Many deployments reserve more GPUs than their workload needs. Tandemn chooses GPU types, parallelism, and replica counts that meet performance targets with less hardware, freeing the remainder for other jobs.

Put Your Entire Fleet to Work

Capacity is often stranded in mixed GPU generations or partially occupied clusters. Tandemn evaluates those resources together, finding useful placements even when no single pool looks like the obvious fit.

Go Where Capacity Is

A full region should not stall a new workload when suitable GPUs are available elsewhere. Tandemn searches across your cloud providers and regions, then places work where capacity, cost, and service needs align.

Make Room for More Workloads

Customer-facing services and batch jobs compete for the same GPUs, but their priorities differ. Tandemn plans them together, protecting response times while admitting up to 22% more online jobs and 40% more batch jobs.

Optimize Every Deployment

Traffic shifts and GPU availability changes, making previous placements too costly. Tandemn revisits deployments as conditions change, adjusting capacity and location to keep performance targets in reach without continual manual retuning.

Get Smarter With Every Deployment

Each deployment produces evidence about how your models behave on your hardware. Tandemn uses those measurements to improve future decisions, reducing repeat benchmarking when demand shifts or a new model enters the fleet.

Your Infrastructure. More Capacity.

Tandemn is open source and runs on Kubernetes inside your environment. It works alongside your existing models and serving engines, so you keep control of your stack, deployment policies, and cloud accounts.