mkinf provides a hosted library of production-ready AI agents and tools for streamlined development and deployment of agentic systems. Developers can integrate pre-built components into their workflows via an SDK or API, accelerating time-to-market. The platform also offers monetization opportunities for contributors and access to a distributed grid of GPU infrastructure for optimized inference performance.
Funding
Funding not disclosed
Founders
Product
Problem
AI model deployment and real-time inference are often hampered by fragmented GPU capacity, leading to high response latency and increased compute costs. Companies can be locked into long-term contracts, struggle to find available hardware, and face infrastructure management hassles.
Solution
mkinf aggregates idle GPU capacity from a distributed network of Tier 3 and 4 data centers, providing a single entry point for accessing compute power. The platform reduces response latency by bringing inference to the edge and minimizes compute costs by allowing users to pay only for what they need. mkinf streamlines AI workload deployment by offering a library of AI tools, models, and agents, collaborative orchestration, and simplified deployment processes.
Target Audience
The primary customers are companies with variable GPU requirements, AI developers, and researchers who need scalable and cost-efficient compute resources for AI model deployment and real-time inference.
Features
- Access to a distributed network of GPUs across multiple data centers.
- Support for various NVIDIA GPUs, including H100, A100, L40s, A40, and A6000.
- On-demand availability of GPU instances with hourly pricing.
- Simplified deployment process with a single endpoint for model deployment.
- Tools for training models, selecting compute resources, and deploying models.
- API access for integrating with existing AI workflows.
- Access to a library of AI tools, models, and agents for plug-and-play integration.