Double Your Effective GPU Capacity

Get more from the GPUs you already have. Tandemn’s open-source planner works with your existing orchestration to optimize self-hosted AI inference across hardware, clouds, and regions—without expanding your fleet.

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Backed and Proven by Research.

Tested against alternative scheduling approaches across diverse workloads, GPU configurations, and performance objectives, Tandemn delivers more usable GPU capacity while simultaneously improving latency, goodput, SLO attainment, and cost efficiency.

Up to 2×GPU utilization

Up to 45%Lower cost per token

A planner for your stack.

Tandemn’s open-source planner expands effective inference capacity by coordinating workloads across isolated clusters, clouds, and regions. It matches workloads to available hardware and tunes serving configurations through your existing stack, so you can run more inference on the GPUs you already have.

Tandemn

ClustersPlan across isolated clusters as one fleet, putting fragmented GPU capacity across clouds and regions to work.

CloudsDistribute workloads across cloud providers to recover capacity stranded in separate cloud deployments.

RegionsPlace workloads across regions to use spare GPU capacity wherever serving targets and placement constraints allow.

HardwareMatch workloads to different GPU types so available memory and compute across your hardware can serve more inference.

FrameworksCoordinate workload placement through NVIDIA Dynamo, Ray, Kubernetes, and custom systems to make spare capacity usable.

EnginesTune parallelism, quantization, and runtime settings in vLLM, SGLang, and other engines to fit more workloads on each GPU.

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Improve All Your Key Metrics.
Simultaneously

GPU optimization usually forces tradeoffs between latency, throughput, utilization, SLOs, and cost. Tandemn continuously adapts GPU selection, parallelism strategies, replica counts, and workload placement to actual demand. Across heterogeneous fleet simulations, it admitted more online and batch jobs while improving SLO-honoring goodput, TTFT attainment, and cost per token.

Admissions More jobs admitted

Goodput Higher useful throughput

Latency Faster first token

Cost Lower cost per token

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All your workloads. All your hardware.
One shared pool.

All your workloads compete for the same shared pool of GPU resources. Optimizing each in isolation leaves capacity on the table. Tandemn jointly plans workload placement and resource allocation, dynamically shifting capacity across hardware, clusters, clouds, and regions as demand changes.

Tandemn

Online

Batch

Embeddings

Vision

TandemnCentral planning layer

GPU types

Clusters

Clouds

Regions

Planned as one

One fleet, allocated in real time.

Tandemn treats fragmented, heterogeneous GPUs as one adaptive capacity pool. As inference demand shifts, it reallocates workloads so more jobs fit on the fleet you already run.

ChatbotEmbeddingsRerankingVision

Shared capacity map99/128 GPU in use

Streamline Model Deployment

Tandemn coordinates model architecture, serving engines, GPU hardware, quantization, and parallelism to find configurations that work together. It handles configuration, placement, scaling, and ongoing optimization, so you can deploy from day one without stitching every layer together by hand.