Hashblock provides bare‑metal GPU servers hosted in Icelandic data centers powered entirely by renewable energy, delivering high‑performance AI training and inference at 40‑50% lower cost than major cloud providers. The service offers flexible IaaS rentals with full hardware maintenance and high‑bandwidth connectivity, enabling AI labs, startups, and enterprise teams to scale compute while minimizing carbon emissions.
Funding
Funding not disclosed
Founders
Product
Problem
AI developers face high costs and significant carbon emissions when using traditional cloud GPU services, which often rely on non-renewable energy and inefficient infrastructure.
Solution
Hashblock offers bare-metal GPU compute hosted in Iceland, where the data center is powered entirely by renewable energy. By operating in a low-cost, energy-efficient location, the platform delivers high-performance GPU resources at 40‑50% lower prices than major cloud providers. Customers rent dedicated GPU servers on a flexible IaaS basis, eliminating the need to manage hardware while benefiting from reduced operational overhead. The service provides a streamlined provisioning process and full hardware maintenance, allowing AI teams to focus on model development and deployment. All compute is delivered with a minimal carbon footprint, aligning AI workloads with sustainability goals.
Target Audience
Primary customers are AI research labs, machine‑learning startups, and enterprise data science teams that require scalable, high‑performance GPU compute while controlling costs and environmental impact.
Features
- Bare-metal GPU servers with direct access to high-performance GPUs for AI training and inference
- 100% renewable energy powering the Icelandic data center, ensuring carbon-neutral compute
- Cost savings of 40‑50% compared to leading cloud providers through efficient operations and strategic location
- Flexible rental terms and simplified management with hardware maintenance handled by Hashblock
- High-bandwidth network connectivity optimized for large AI workloads