Configuration
Match GPU types, serving configurations, and replica counts to each workload’s latency targets, priorities, and resource requirements.
PyTorch Conference 2026
Built to double your capacity.
Run more workloads on the GPUs you already have. Tandemn is one open-source planner that coordinates your entire fleet, from workload configuration and placement to execution through your existing infrastructure. Our goal is 2× effective capacity: twice the inference workload on the same hardware, while meeting serving targets. Meet us at PyTorch Conference to see how.
October 20–21, 2026 · San Jose, California
More workloads. Same fleet.
Turn fragmented capacity into room for more inference. Tandemn plans workload configuration and placement together across deployments, hardware types, and resource pools, fitting more useful work into your fleet while accounting for each model’s serving requirements.
Placement
Plan across heterogeneous GPUs, clusters, clouds, and regions together, putting fragmented capacity to work for online and batch inference.
Learning
Use observed serving performance to refine the planner’s predictions and adapt configurations and placements as workloads and infrastructure change.
One planner. Your entire stack.
Tandemn is self-hosted in your VPC or on-premises environment. It works alongside Kubernetes, NVIDIA Dynamo, Ray, vLLM, and SGLang, connecting fleet-wide decisions to the orchestration and serving systems your team already operates. Models, workload data, and serving telemetry stay under your control.
Explore the productA smarter plan for more capacity.
Tandemn learns from serving performance and continually refines how workloads fit together. Configuration, placement, and execution work as one system, adapting as demand and infrastructure change. Every decision supports the same ambition: double effective capacity and run more workloads without expanding your GPU fleet.
Make room for more workloads.
Tell us what you want to run and the GPU fleet you have today. Meet with our team at PyTorch Conference to see how Tandemn can unlock more capacity, connect to your existing stack, and help you serve more inference from the same hardware.
You can also reach us at hello@tandemn.com.