Glossary OS

Leitura rápida

Comece pela explicação mais curta e útil antes de aprofundar.

A decentralized GPU compute marketplace built on Solana that connects GPU providers with users needing compute for AI inference workloads. Node operators supply idle GPU capacity and earn NOS tokens for completed jobs. Nosana focuses on cost-effective AI inference rather than training, using Solana for job coordination, payment settlement, and reputation tracking. It supports containerized workloads across consumer and enterprise GPUs.

Modelo mental

Use primeiro a analogia curta para raciocinar melhor sobre o termo quando ele aparecer em código, docs ou prompts.

Pense nisso como uma peça da pilha de contexto ou inferência usada em produtos com agentes ou LLMs.

Contexto técnico

Coloque o termo dentro da camada de Solana em que ele vive para raciocinar melhor sobre ele.

LLMs, RAG, embeddings, inferência e primitivas voltadas a agentes.

Por que builders ligam para isso

Transforme o termo de vocabulário em algo operacional para produto e engenharia.

Este termo destrava conceitos adjacentes rapidamente, então funciona melhor quando você o trata como um ponto de conexão, não como definição isolada.

Handoff para IA

Use este bloco compacto quando quiser dar contexto aterrado para um agente ou assistente sem despejar a página inteira.

Nosana (nosana)
Categoria: IA / ML
Definição: A decentralized GPU compute marketplace built on Solana that connects GPU providers with users needing compute for AI inference workloads. Node operators supply idle GPU capacity and earn NOS tokens for completed jobs. Nosana focuses on cost-effective AI inference rather than training, using Solana for job coordination, payment settlement, and reputation tracking. It supports containerized workloads across consumer and enterprise GPUs.
Relacionados: GPU Compute (Decentralized), Decentralized Inference, DePIN (Redes de Infraestrutura Física Descentralizada)

Glossary Copilot

Faça perguntas de Solana com contexto aterrado sem sair do glossário.

Use contexto do glossário, relações entre termos, modelos mentais e builder paths para receber respostas estruturadas em vez de output genérico.

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Pergunta

Explicar este código

Opcional: cole código Anchor, Solana ou Rust para o Copilot mapear primitivas de volta para termos do glossário.

Faça uma pergunta aterrada no glossário

O Copilot vai responder usando o termo atual, conceitos relacionados, modelos mentais e o grafo ao redor do glossário.

Grafo conceitual

Veja o termo como parte de uma rede, não como uma definição sem saída.

Esses ramos mostram quais conceitos esse termo toca diretamente e o que existe uma camada além deles.

Ramo

GPU Compute (Decentralized)

Blockchain-coordinated networks that aggregate GPU resources for AI training and inference. Projects like Render Network and io.net on Solana allow GPU owners to rent out compute to AI researchers and developers. This democratizes access to expensive GPU hardware needed for AI workloads. Token incentives align supply (GPU providers) with demand (AI developers).

Training (ML)Inference

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Ramo

Decentralized Inference

Running AI model inference across distributed networks of GPU providers rather than centralized cloud infrastructure, using blockchain for coordination, payment, and verification. Key verification approaches include ZKML (zero-knowledge proofs of correct inference) and trusted execution environments (TEEs). Projects include Bittensor, Render Network, and io.net on Solana.

Bittensor (TAO)On-Chain AI / ML

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Ramo

DePIN (Redes de Infraestrutura Física Descentralizada)

Blockchain protocols that coordinate and incentivize physical infrastructure through token rewards. DePIN projects on Solana include: Helium (wireless networks), Render (GPU rendering), Hivemapper (mapping), and io.net (distributed GPU compute for AI). Contributors provide physical resources (hardware, bandwidth) and earn tokens. DePIN bridges blockchain economics with real-world infrastructure.

Blockchain

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Próximos conceitos para explorar

Continue a cadeia de aprendizado em vez de parar em uma única definição.

Estes são os próximos conceitos que valem abrir se você quiser que este termo faça mais sentido dentro de um workflow real de Solana.

IA / ML

GPU Compute (Decentralized)

Blockchain-coordinated networks that aggregate GPU resources for AI training and inference. Projects like Render Network and io.net on Solana allow GPU owners to rent out compute to AI researchers and developers. This democratizes access to expensive GPU hardware needed for AI workloads. Token incentives align supply (GPU providers) with demand (AI developers).

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IA / ML

Decentralized Inference

Running AI model inference across distributed networks of GPU providers rather than centralized cloud infrastructure, using blockchain for coordination, payment, and verification. Key verification approaches include ZKML (zero-knowledge proofs of correct inference) and trusted execution environments (TEEs). Projects include Bittensor, Render Network, and io.net on Solana.

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IA / ML

DePIN (Redes de Infraestrutura Física Descentralizada)

Blockchain protocols that coordinate and incentivize physical infrastructure through token rewards. DePIN projects on Solana include: Helium (wireless networks), Render (GPU rendering), Hivemapper (mapping), and io.net (distributed GPU compute for AI). Contributors provide physical resources (hardware, bandwidth) and earn tokens. DePIN bridges blockchain economics with real-world infrastructure.

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IA / ML

On-Chain AI / ML

Running AI/ML inference directly within blockchain smart contracts or verified through on-chain proofs. Current limitations: compute budgets on blockchains are tiny compared to AI needs. Approaches include: off-chain inference with on-chain verification (ZK proofs of inference), optimistic verification, and simplified models (decision trees, linear models) that fit within compute limits.

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Termos relacionados

Siga os conceitos que realmente dão contexto a este termo.

Entradas de glossário só ficam úteis quando estão conectadas. Esses links são o caminho mais curto para ideias adjacentes.

IA / MLgpu-compute

GPU Compute (Decentralized)

Blockchain-coordinated networks that aggregate GPU resources for AI training and inference. Projects like Render Network and io.net on Solana allow GPU owners to rent out compute to AI researchers and developers. This democratizes access to expensive GPU hardware needed for AI workloads. Token incentives align supply (GPU providers) with demand (AI developers).

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IA / MLdecentralized-inference

Decentralized Inference

Running AI model inference across distributed networks of GPU providers rather than centralized cloud infrastructure, using blockchain for coordination, payment, and verification. Key verification approaches include ZKML (zero-knowledge proofs of correct inference) and trusted execution environments (TEEs). Projects include Bittensor, Render Network, and io.net on Solana.

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IA / MLdepin

DePIN (Redes de Infraestrutura Física Descentralizada)

Blockchain protocols that coordinate and incentivize physical infrastructure through token rewards. DePIN projects on Solana include: Helium (wireless networks), Render (GPU rendering), Hivemapper (mapping), and io.net (distributed GPU compute for AI). Contributors provide physical resources (hardware, bandwidth) and earn tokens. DePIN bridges blockchain economics with real-world infrastructure.

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Mais na categoria

Permaneça na mesma camada e continue construindo contexto.

Essas entradas vivem ao lado do termo atual e ajudam a página a parecer parte de um grafo maior, não um beco sem saída.

IA / ML

LLM (Modelo de Linguagem Grande)

A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.

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IA / ML

Transformer

The neural network architecture underlying modern LLMs, introduced in 'Attention Is All You Need' (2017). Transformers use self-attention mechanisms to process input sequences in parallel (unlike recurrent networks). Key components: multi-head attention, positional encoding, feedforward layers, and layer normalization. Variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5).

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IA / ML

Attention Mechanism

A neural network component that allows models to weigh the relevance of different parts of the input when producing output. Self-attention computes query-key-value dot products across all positions, enabling each token to 'attend' to every other token. Multi-head attention runs multiple attention functions in parallel. Attention is O(n²) in sequence length, driving context window research.

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IA / ML

Foundation Model

A large AI model trained on broad data that can be adapted for many downstream tasks. Foundation models (GPT-4, Claude, Llama 3, Gemini) are pre-trained on internet-scale text/code and can be fine-tuned, prompted, or used via APIs for specific applications. The term emphasizes that one base model serves as the foundation for diverse use cases rather than training task-specific models.

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