Tandemn’s planning algorithm helps teams get more from their existing GPUs. In a three-hour heterogeneous-cluster simulation, it delivered 45% more throughput at 31% lower cost per token, while improving job admission and SLO attainment across evaluations.
Cost-optimal LLM serving is a joint optimization across six dimensions — model × workload shape × GPU type × parallelism (TP/PP) × engine settings × cloud topology.
A practical walk-through of the hidden AWS networking stack behind fast multinode inference on A100 and L40S clusters.
A benchmark sweep showing why cost-optimal LLM inference is a live optimization problem across TP, PP, GPU count, SLO, and workload shape.
A deep dive into what actually drives cost and throughput in self-hosted LLM inference — and why most clusters silently overpay 2–5×.
A case study in catalog enrichment, attribute extraction, and the infrastructure problem underneath.
Tandemn has raised $1.7 million to build open infrastructure for running AI workloads across heterogeneous compute.