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PANIM Emerges from Stealth to Build Distributed Infrastructure for the Next Generation of Artificial Intelligence
PANIM
Mon, 13 July 2026 at 1:30 pm GMT+5:30
5 min read
LONDON, July 13, 2026 (GLOBE NEWSWIRE) — As artificial intelligence continues its rapid expansion, the industry’s greatest challenge is no longer building more capable models—it is building the infrastructure required to run them efficiently at global scale
PANIM AI System Limited, a UK technology company headquartered in England, today announced the public launch of PANIM, a distributed AI infrastructure platform designed to support batched, latency-tolerant AI workloads through a network of operator-hosted edge devices, secure scheduling and protocol-driven coordination
Rather than competing with hyperscale cloud providers, PANIM has been developed around a different premise: the future AI ecosystem will require multiple infrastructure layers operating together. While centralized data centers remain essential for training frontier models and real-time inference, an increasing number of AI workloads can be executed more efficiently through geographically distributed compute networks
This idea became the foundation of PANIM
Building Infrastructure Rather Than Another AI Model
For more than a decade, progress in artificial intelligence has been measured by increasingly powerful foundation models. Yet behind every AI system lies an expanding industrial supply chain involving computation, networking, data collection, energy consumption and secure workload execution
PANIM’s founders believe the industry’s next phase will be defined less by larger models than by better infrastructure
Instead of developing another large language model, the company has focused on building systems that allow AI computation to be distributed, verified and economically coordinated across independent operators
According to the PANIM Protocol whitepaper, the network combines regional schedulers, stateless mesh routers and operator-hosted X-HUB devices under a dedicated communication protocol specifically designed for batched AI workloads. The protocol deliberately avoids competing with traditional hyperscale infrastructure, positioning distributed computing as a complementary layer rather than a replacement for conventional cloud services
Unlike many infrastructure platforms that expose functionality through conventional cloud APIs, PANIM was designed around a dedicated protocol
The company argues that protocol-driven architecture provides greater transparency for workload scheduling, stronger cryptographic guarantees and better interoperability across independently operated hardware. Workloads remain encrypted until execution, scheduling decisions are coordinated through short-lived cryptographic leases, and hardware identity is verified through secure attestation before computation begins
