Why Decentralized AI?

The capabilities of foundation models continue to advance rapidly, yet the infrastructure powering them remains highly centralized. Most large-scale AI models today are trained, hosted, and served by a small number of cloud providers and private labs. While these centralized systems deliver strong performance, they come with structural limitations that increasingly constrain access, transparency, and trust.

Source : Langchain 2024 State of AI, https://blog.langchain.com/langchain-state-of-ai-2024/

For many users, even open-source models are effectively out of reach. The infrastructure required to serve LLaMA or Mixtral in production—high-memory GPUs, low-latency scheduling, and scalable batching systems—remains tightly coupled to closed deployments. This creates a growing divide between those who can build with cutting-edge models and those who cannot.

More importantly, centralization introduces serious risks. Users must trust that their data will not be stored or repurposed. Developers cannot audit how a model was hosted, fine-tuned, or changed over time. Access can be revoked without warning, and outages from a single provider can bring down entire applications.

Decentralized AI offers a compelling alternative. It promises an infrastructure where models are distributed, governance is community-driven, and execution can happen on commodity hardware. In theory, this would enable broader participation, stronger resilience, and more accountable deployment of AI. But in practice, decentralizing AI has proven difficult.


Why Existing Approaches Fall Short

Many decentralized AI proposals to date fall into one of the following categories:

  • Off-chain inference coordination. These systems use token incentives or protocol wrappers, but the actual model execution happens on opaque servers. There is no way to verify whether the model was run correctly, or at all.

  • Heavyweight cryptographic protocols. Secure multiparty computation or fully homomorphic encryption can theoretically protect privacy, but they are too slow for real-time inference on models with millions of parameters.

  • Poor system integration. Even when model execution is distributed, it often requires manual setup, inconsistent runtimes, or high developer overhead. These systems lack orchestration mechanisms that work across nodes and workloads.

  • Incentive misalignment. Some systems reward output volume without measuring correctness, which encourages junk inference or unverifiable outputs. Others provide no mechanism to penalize bad actors or prioritize quality.

These limitations are not just technical edge cases—they reflect a deeper mismatch between vision and execution. Despite strong theoretical motivation, most decentralized AI systems cannot serve large models at scale, under latency and trust constraints that real users care about.


The Path Forward

To make decentralized AI viable, the system must do more than distribute tasks. It must support:

  • Efficient model partitioning that enables devices with modest resources to contribute meaningfully

  • Scheduling and routing that adapts to dynamic node availability and hardware heterogeneity

  • Verification and reproducibility, so that outputs can be trusted without relying on the executor

  • Robust economic design, aligning incentives with accuracy, uptime, and throughput

The infrastructure must be capable of hosting real-world models—such as transformers, RAG pipelines, and diffusion architectures—while preserving usability and scalability.

In the following sections, we introduce Nesa’s approach to these challenges, starting with a sharding framework that lowers the hardware barrier to serving large models.

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