Tandemn Raises $1.7M to Build the Missing Layer for AI Infrastructure

We are excited to share that Tandemn has raised $1.7 million to build open infrastructure for running AI workloads across heterogeneous compute.

The round includes Jackson Square Ventures, Precursor Ventures, SHAKTI, Afore Capital, OneSixOne Ventures, LeapYear, Illini Angels, and Illinois Ventures, alongside people who have believed in this direction from the beginning. We are grateful for their trust, and for the operators, researchers, engineers, and friends who have kept pushing on the same problem with us: the world has an enormous amount of accelerator capacity, and too much of it is still hard to use well.

The problem

AI infrastructure is being pulled in two directions at once.

On one side, models keep getting larger, more capable, and more demanding. Teams want to run inference, batch jobs, fine-tuning, experiments, evaluations, and training-adjacent workloads without thinking about every GPU, region, failure mode, and placement decision by hand.

On the other side, the hardware world is becoming more fragmented. Production fleets increasingly span NVIDIA, AMD, Intel, cloud GPUs, private clusters, older accelerator generations, reserved nodes, spot nodes, and custom silicon. That compute is valuable, but it is often underutilized because the software layer above it is too rigid.

The gap between those two realities is where Tandemn lives.

What we are building

Tandemn is the missing layer between underutilized accelerators and massive models.

Our goal is to make heterogeneous infrastructure behave like one coordinated system. That means zero-friction scheduling, KV-block teleportation, and ultra-light messaging that allow different accelerators, runtimes, and clusters to cooperate in real time.

We care about this because the next generation of AI infrastructure will not be solved by assuming every workload runs on the same perfect box. It will be solved by software that understands workload shape, latency targets, throughput targets, cost constraints, available capacity, and the actual behavior of the underlying hardware.

If we do this correctly, teams should be able to bring the models, clouds, clusters, and accelerators they already use. Tandemn should make the system more efficient without forcing a rewrite of the stack around it.

Why now

The utilization problem is becoming impossible to ignore.

Companies are buying more accelerators, renting more cloud capacity, and still leaving performance on the table. At the same time, open models are moving faster than most infrastructure teams can re-tune their deployments. A configuration that works for one model, GPU, batch shape, or traffic pattern can become wasteful as soon as any part of that system changes.

This is not just a scheduling problem. It is a live systems problem.

Tandemn is being built to observe, adapt, and coordinate across the whole execution environment. We want inference, training, and everything in between to become easier to place, easier to move, easier to recover, and easier to run at high utilization.

Building in the open

We believe this layer should be built with the open-source community.

Code, benchmarks, and integrations are on the way. We are especially excited about the work happening around KV-cache mobility, distributed serving, and the broader ecosystem for efficient model execution. We will share more soon, including collaborations with LMCache Lab and n0computer.

For now, this funding gives us room to keep building the core systems work: scheduling, runtime coordination, networking, cache movement, telemetry, and the developer experience around all of it.

Join us

We are looking for cracked engineers and interns in distributed systems, networking, and ML systems.

If you care about GPU utilization, low-latency inference, cache-aware scheduling, placement across heterogeneous hardware, or the strange details that make real clusters fast, we would love to talk.

Thank you again to Jackson Square Ventures, Precursor Ventures, SHAKTI, Afore Capital, OneSixOne Ventures, LeapYear, Illini Angels, Illinois Ventures, and everyone who has helped us get here.

More soon.

• Tandemn Labs