Boltzmann Network is a decentralized AI platform that utilizes peer-to-peer processing and onion routing to ensure privacy and data ownership while minimizing latency. It addresses the limitations of traditional AI systems by enabling scalable, secure, and transparent AI inference tasks across a distributed network of nodes.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional cloud infrastructure wasn't designed for the unique demands of AI inference, leading to performance bottlenecks, unpredictable costs, and complex deployments. Developers struggle with outdated systems, rising GPU costs, and a lack of transparency in compute usage, hindering efficient scaling and innovation.
Solution
Boltzmann provides a vertically-optimized platform for AI inference, offering a decentralized network that distributes AI models and processing across secure nodes. This approach enables individual devices to harness expansive compute power through collaborative task processing in a fully decentralized environment. By optimizing routing and model sharding, Boltzmann maximizes throughput while minimizing latency. The platform offers transparent economics with clear, predictable pricing and full visibility into model execution. It allows teams to build inference workflows like pipelines, choosing compute locations and optimizing for latency or throughput, all while maintaining complete auditability and control over model execution.
Target Audience
Boltzmann targets AI engineers, enterprise teams, and product teams seeking to deploy AI models at scale with full control, transparent economics, and minimal infrastructure overhead.
Features
- Decentralized AI inference network leveraging peer-to-peer processing
- Onion routing and enhanced encryption for comprehensive privacy protection
- Dynamic scaling to meet performance demands, minimizing latency
- Transparent economics with clear, predictable pricing
- Model sharding and optimized routing for maximum throughput
- Vertically-optimized platform purpose-built for serving large language models and transformer architectures
- Complete visibility into model execution, including hardware usage and performance metrics
- Support for building inference workflows as pipelines, enabling composability