Enterprise GPU swarms outperform cloud A100s by 37%
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Companies everywhere face ballooning cloud costs and increasing pressure to meet net-zero targets. Hidden within everyday corporate desktops, workstations, and servers is an untapped opportunity. A recent proof of concept (PoC) demonstrated that a distributed "swarm" of consumer-grade GPUs can match (and frequently surpass) premium cloud GPUs for enterprise AI workloads.
A different kind of cloud test
In collaboration with a global bank, we explored whether standard corporate GPUs could replace cloud-hosted AI inference. The PoC compared workstation GPUs (NVIDIA RTX 4500, RTX 4090, and dual RTX 6000 Ada) with Runpod’s high-performance A100 80 GB GPU instance.
Inside the GPU swarm
Hivenet transforms idle corporate GPUs into a secure, enterprise-ready computing network. Managed through a lightweight gateway, this distributed cluster scales effortlessly on demand, encrypts all communications, and integrates smoothly with existing enterprise identity services, without requiring any new hardware.
PoC compute results
The testing involved generative AI inference workloads, meticulously tracking key metrics such as throughput (tokens per second), latency, concurrency, and energy efficiency.
The swarm's 2x dual RTX 6000 Ada GPUs notably outperformed Runpod's 2xA100 by achieving 37% higher throughput at peak load and maintaining a consistent 16% throughput advantage under continuous workloads. While the A100 held a slight latency advantage (11% better time-to-first-token at extremely high concurrency), the GPU cluster running on Hivenet tech provided impressive performance overall. Energy usage was initially higher for the consumer GPUs. But after factoring in typical data center overheads (PUE), the power efficiency gap significantly narrowed.
Cost efficiency and savings
Enterprises need concrete financial evidence to inform strategic decisions, and the data speaks clearly. Monthly total cost of ownership (TCO)—covering hardware amortization over three years (based on typical enterprise hardware lifecycles), energy priced at €0.18/kWh (based on average price in 2024), and associated licensing or cloud fees—was calculated with realistic assumptions of 75% GPU utilization.
This GPU swarm significantly reduces costs, delivering savings of approximately 49% compared to cloud-hosted GPUs and around 43% against traditional on-premises A100 setups. Lower-tier GPUs like the RTX 4500 or RTX 4090 can drive costs further down for less latency-sensitive workloads.
Why every CIO should care
The results of this PoC represent a technical achievement and signal a transformative shift in enterprise computing strategy. By converting underutilized corporate hardware into high-performance AI infrastructure, businesses can liberate substantial budget resources and immediately redirect these savings into innovation, talent acquisition, or critical business growth initiatives.
Relying on owned infrastructure brings predictability and stability in latency and throughput, avoiding issues common with cloud region congestion or unexpected price fluctuations. Enterprises in regulated sectors particularly benefit, as running inference workloads on-site significantly simplifies data sovereignty compliance.
Beyond cost savings, distributed GPU swarms deliver tangible sustainability benefits. Reusing existing hardware drastically reduces the environmental impact of new data center builds and lowers ongoing energy demands, directly contributing to corporate ESG commitments.
By leveraging their hardware more strategically, enterprises can also strengthen negotiating positions with cloud providers, ensuring better terms and avoiding vendor lock-in with a low-risk integration model that complements existing infrastructure (using lightweight containers, API endpoints, and secure VPN tunneling for deployment) where workloads dynamically reassign if any node becomes unavailable. This approach offers operational resilience without additional complexity.
"We saw immediate relief in our GPU budget," said a senior infrastructure lead at the participating bank after the test. "The transition was smoother than expected, and the performance surprised our engineering team."
Distributed GPU clusters offer a strategic edge, transforming idle corporate assets into productive, high-value resources. This saves money AND gives businesses more control, better sustainability, and a stronger, more flexible AI system.
Strategic takeaway
Instead of continually renting expensive cloud GPUs, enterprises now have a feasible, immediately actionable alternative. Hivenet’s distributed GPU swarm technology demonstrates conclusively that using existing desktops is viable, and it's the most practical and cost-effective path to efficient, sustainable, and secure AI infrastructure.
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