{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-petals","slug":"petals","name":"Petals","type":"repo","url":"https://github.com/bigscience-workshop/petals","page_url":"https://unfragile.ai/petals","categories":["deployment-infra"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-petals__cap_0","uri":"capability://automation.workflow.peer.to.peer.distributed.model.inference","name":"peer-to-peer distributed model inference","description":"Enables inference on large language models by distributing computation across a peer-to-peer network using BitTorrent-style protocols. Each peer runs a subset of model layers, and inference requests are routed through the network with automatic layer assignment and load balancing. Uses a DHT (Distributed Hash Table) for peer discovery and maintains connection pools to optimize throughput across heterogeneous hardware.","intents":["Run inference on models too large for a single GPU without paying for cloud inference APIs","Contribute spare GPU capacity to a distributed network and earn rewards","Build applications that leverage distributed inference without managing infrastructure","Reduce inference latency by parallelizing layer computation across geographically distributed peers"],"best_for":["researchers and developers working with models >7B parameters on limited hardware","organizations seeking cost-effective inference alternatives to centralized cloud providers","GPU-rich institutions wanting to monetize idle compute capacity","builders of latency-sensitive applications requiring distributed execution"],"limitations":["Network latency between peers adds 50-200ms per forward pass depending on peer distance and bandwidth","Requires minimum GPU VRAM to hold at least one model layer; very small GPUs (<2GB) may not participate effectively","No built-in fault tolerance for peer disconnections mid-inference; requires client-side retry logic","Inference speed degrades with network congestion; not suitable for real-time applications requiring <100ms latency","Peer availability is non-deterministic; inference may fail if peers holding required layers go offline"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA support (for GPU acceleration)","Stable internet connection with sufficient bandwidth (minimum 10 Mbps recommended)","GPU with at least 1GB VRAM for meaningful participation as a peer","Access to Petals network (requires joining DHT or connecting to bootstrap nodes)"],"input_types":["text prompts (tokenized as input_ids)","structured inference parameters (temperature, top_k, max_length)","model identifiers (HuggingFace model names)"],"output_types":["token sequences (generated text)","logits (raw model outputs for custom decoding)","generation metadata (tokens generated, inference time)"],"categories":["automation-workflow","distributed-computing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-petals__cap_1","uri":"capability://automation.workflow.adaptive.layer.routing.and.load.balancing","name":"adaptive layer routing and load balancing","description":"Dynamically assigns model layers to available peers based on real-time metrics including peer bandwidth, GPU utilization, latency, and VRAM availability. Uses a greedy routing algorithm that selects the optimal peer for each layer during inference, with fallback mechanisms for peer unavailability. Maintains a peer registry with periodic health checks and bandwidth estimation via probe requests.","intents":["Ensure inference completes even when some peers are slow or offline by routing around bottlenecks","Maximize throughput by assigning layers to peers with the best bandwidth and lowest latency","Balance load across peers to prevent any single peer from becoming a bottleneck","Adapt to changing network conditions without requiring manual configuration"],"best_for":["applications requiring reliable inference across unstable or heterogeneous networks","operators managing large peer pools with varying hardware capabilities","use cases where inference latency is secondary to reliability and throughput"],"limitations":["Routing decisions are made per-inference and don't account for future peer state changes; may select suboptimal peers if network conditions change mid-inference","Health checks add overhead (~5-10ms per peer per check interval); frequent checks improve accuracy but increase network traffic","No global optimization across concurrent inferences; each request independently selects peers, potentially causing contention","Bandwidth estimation via probes is approximate and may not reflect actual throughput under load"],"requires":["Active peer registry with at least 2-3 peers per model layer for redundancy","Network connectivity allowing bidirectional communication between client and all peers","Periodic health check mechanism (typically 10-30 second intervals)"],"input_types":["peer metadata (VRAM, bandwidth, latency)","layer requirements (size, compute intensity)","inference request parameters (batch size, sequence length)"],"output_types":["routing decisions (peer assignments per layer)","latency estimates (predicted inference time)","load metrics (peer utilization, queue depth)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-petals__cap_10","uri":"capability://text.generation.language.client.side.inference.orchestration.and.context.management","name":"client-side inference orchestration and context management","description":"Provides client libraries (Python, JavaScript) that handle inference orchestration, including prompt tokenization, layer routing, result decoding, and error handling. Manages inference context including conversation history, system prompts, and generation parameters. Implements client-side caching of tokenized prompts to avoid re-tokenization. Abstracts away network complexity, presenting a simple API similar to standard LLM inference libraries.","intents":["Use Petals for inference without understanding distributed architecture details","Manage conversation context and multi-turn interactions","Cache tokenized prompts to avoid redundant tokenization","Handle errors and retries transparently"],"best_for":["application developers building on top of Petals","non-infrastructure teams wanting to use distributed inference without managing peers","prototyping and experimentation with distributed models"],"limitations":["Client libraries add abstraction overhead; advanced users may need lower-level APIs","Context management is client-side only; no server-side session persistence","Tokenization is model-specific; requires correct tokenizer for each model","No built-in support for multi-model inference in a single request"],"requires":["Python 3.8+ or Node.js 14+ (depending on client library)","Petals network access (bootstrap node or peer list)","Model-specific tokenizer (HuggingFace transformers library)"],"input_types":["text prompts","generation parameters (temperature, top_k, max_length)","conversation history (for multi-turn interactions)"],"output_types":["generated text","token sequences","generation metadata"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-petals__cap_11","uri":"capability://automation.workflow.model.agnostic.layer.distribution.and.compatibility","name":"model-agnostic layer distribution and compatibility","description":"Supports any transformer-based model that can be split into layers, regardless of architecture (BERT, GPT, LLaMA, Mistral, etc.). Automatically detects model structure and layer boundaries from HuggingFace model configs. Handles different layer types (attention, feed-forward, embedding) transparently. Includes compatibility layer for models with non-standard architectures or custom layers. Supports both encoder-only and decoder-only models.","intents":["Run any transformer model on Petals without model-specific modifications","Support new models as they're released without code changes","Mix models from different families in the same network","Handle custom or proprietary model architectures"],"best_for":["researchers experimenting with different model architectures","applications requiring flexibility to switch between models","deployments supporting multiple model families"],"limitations":["Non-transformer models (CNNs, RNNs) are not supported","Custom layers not in standard PyTorch may require custom serialization","Model-specific optimizations (e.g., flash attention) may not be available across all peers","Automatic layer detection may fail for unusual model structures; requires manual configuration"],"requires":["HuggingFace model config (config.json)","Model weights in standard PyTorch format","Transformer architecture (BERT, GPT, etc.)"],"input_types":["model identifiers (HuggingFace model names)","model configs (architecture, layer sizes)","model weights (PyTorch tensors)"],"output_types":["layer assignments (which layers run on which peers)","compatibility metadata (supported features, limitations)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-petals__cap_2","uri":"capability://memory.knowledge.model.layer.caching.and.prefetching","name":"model layer caching and prefetching","description":"Caches model layers locally on peers to avoid re-downloading them for subsequent inferences. Implements LRU (Least Recently Used) eviction policy with configurable cache size based on available VRAM. Prefetches layers before inference begins based on predicted request patterns, reducing latency for common model paths. Uses content-addressable storage (hashing) to verify layer integrity and enable deduplication across peers.","intents":["Reduce bandwidth consumption by caching frequently-accessed layers locally","Improve inference latency by prefetching layers before they're needed","Enable peers with limited VRAM to participate by caching only the most frequently used layers","Verify layer integrity and prevent corrupted weights from affecting inference"],"best_for":["peers with limited VRAM that can't hold entire models but have sufficient disk space","applications with predictable inference patterns (e.g., always using the same model)","networks with high bandwidth costs where reducing data transfer is critical"],"limitations":["Cache misses still require full layer download; no partial layer caching or compression","Prefetching requires accurate prediction of inference patterns; incorrect predictions waste bandwidth","LRU eviction doesn't account for layer size or download time; may evict large layers that are expensive to re-download","Cache coherency is not guaranteed across peers; stale layers may be served if model weights are updated"],"requires":["Persistent storage (disk or SSD) with capacity >= largest model layer size","Configurable cache size parameter (typically 10-100GB depending on peer hardware)","Hash verification mechanism (SHA-256 or similar) for layer integrity"],"input_types":["model layer data (binary weights)","layer metadata (size, hash, model version)","access patterns (historical inference requests)"],"output_types":["cached layer data","cache hit/miss metrics","prefetch recommendations"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-petals__cap_3","uri":"capability://automation.workflow.heterogeneous.hardware.support.with.automatic.precision.selection","name":"heterogeneous hardware support with automatic precision selection","description":"Automatically selects appropriate numerical precision (FP32, FP16, INT8) for each layer based on peer hardware capabilities and model requirements. Handles mixed-precision inference where different layers run at different precisions on different peers. Includes quantization support for reducing VRAM requirements on resource-constrained peers. Detects hardware capabilities (GPU type, compute capability, available VRAM) and adapts layer execution accordingly.","intents":["Enable older GPUs and CPUs to participate in inference by using lower precision","Reduce VRAM requirements for large models by quantizing layers on peers with limited memory","Maximize throughput by using the highest precision that hardware can support","Support inference across heterogeneous hardware without manual configuration"],"best_for":["networks with diverse hardware (mix of RTX, A100, older GPUs, CPUs)","applications where inference accuracy is less critical than accessibility","operators wanting to maximize peer participation regardless of hardware age"],"limitations":["Quantization introduces accuracy loss; INT8 quantization typically reduces accuracy by 1-5% depending on model","Mixed-precision inference requires careful handling of layer boundaries to avoid numerical instability","Automatic precision selection may not match manual tuning for specific hardware; requires profiling to optimize","Not all layers benefit equally from quantization; some layers (attention heads) are more sensitive to precision loss"],"requires":["Hardware with quantization support (most modern GPUs; CPU inference is slower)","Quantized model weights (pre-computed or generated on-the-fly)","Hardware capability detection library (e.g., CUDA compute capability detection)"],"input_types":["hardware specifications (GPU type, VRAM, compute capability)","model layer characteristics (size, sensitivity to quantization)","precision preferences (user-specified or auto-detected)"],"output_types":["precision assignments per layer","quantized weights (if applicable)","performance estimates (throughput, latency, accuracy impact)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-petals__cap_4","uri":"capability://tool.use.integration.dht.based.peer.discovery.and.bootstrap","name":"dht-based peer discovery and bootstrap","description":"Uses a Distributed Hash Table (DHT) similar to BitTorrent to discover peers offering specific model layers without requiring a central server. Peers register themselves in the DHT with their available layers, VRAM, and bandwidth. Clients query the DHT to find peers capable of serving requested layers. Includes bootstrap node mechanism for initial network entry and fallback peer lists for network resilience.","intents":["Discover peers offering specific model layers without relying on a central registry","Join the Petals network without knowing any existing peers","Maintain network resilience by allowing peers to join/leave dynamically","Enable clients to find alternative peers if their current selection becomes unavailable"],"best_for":["truly decentralized deployments without central infrastructure","networks expecting high peer churn (peers frequently joining/leaving)","applications requiring bootstrap without pre-configured peer lists"],"limitations":["DHT lookups add 100-500ms latency compared to direct peer lists; not suitable for latency-critical applications","DHT is eventually consistent; newly registered peers may not appear in queries for 30-60 seconds","Sybil attacks are possible if DHT is not protected; malicious peers can register themselves as offering layers they don't actually have","Bootstrap nodes are single points of failure; network may become partitioned if all bootstrap nodes go offline"],"requires":["Network connectivity to at least one bootstrap node","DHT implementation (Petals uses a custom DHT based on Kademlia protocol)","Periodic peer registration refresh (typically every 30-60 minutes)"],"input_types":["model layer identifiers (model name + layer index)","peer metadata (VRAM, bandwidth, latency)","bootstrap node addresses (IP:port)"],"output_types":["peer lists (addresses of peers offering requested layers)","peer metadata (VRAM, bandwidth, latency estimates)","network topology information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-petals__cap_5","uri":"capability://text.generation.language.streaming.token.generation.with.early.stopping","name":"streaming token generation with early stopping","description":"Streams generated tokens back to the client as they're produced rather than waiting for full sequence completion. Implements early stopping mechanisms allowing clients to terminate generation mid-sequence if desired (e.g., when reaching a stop token or max length). Uses token-by-token routing where each generated token is fed back through the network for the next iteration, with caching of intermediate states to reduce redundant computation.","intents":["Provide real-time feedback to users by streaming tokens as they're generated","Reduce latency for applications that only need partial outputs (e.g., first few tokens)","Enable interactive applications where users can interrupt generation","Reduce total inference time by stopping early when stop conditions are met"],"best_for":["interactive applications (chatbots, code completion) requiring real-time feedback","applications with variable output length where early stopping saves computation","use cases where user experience is improved by progressive token delivery"],"limitations":["Token-by-token routing adds latency per token (50-200ms) compared to batch inference; slower for generating long sequences","Streaming requires maintaining client connection throughout generation; network interruptions lose partial output","Early stopping logic must be implemented on client side; no server-side enforcement of stop conditions","Intermediate state caching adds memory overhead on peers; not suitable for very long sequences"],"requires":["Bidirectional communication channel (WebSocket or similar) supporting streaming","Client-side token processing logic","Stop token definitions or max_length parameters"],"input_types":["initial prompt (text or tokens)","generation parameters (temperature, top_k, max_length, stop_tokens)","streaming preferences (chunk size, timeout)"],"output_types":["token stream (individual tokens as they're generated)","generation metadata (tokens generated so far, estimated time remaining)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-petals__cap_6","uri":"capability://safety.moderation.incentive.mechanism.and.peer.reputation.tracking","name":"incentive mechanism and peer reputation tracking","description":"Tracks peer reputation based on inference quality, availability, and response time. Implements incentive mechanisms (rewards, penalties) to encourage high-quality participation and discourage malicious or low-quality peers. Maintains reputation scores updated based on inference success/failure, latency measurements, and user feedback. Integrates with optional blockchain or token systems for monetizing peer contributions.","intents":["Encourage peers to maintain high availability and low latency by rewarding good behavior","Identify and deprioritize low-quality peers that produce incorrect results or high latency","Monetize peer contributions by distributing rewards for successful inferences","Prevent Sybil attacks by requiring reputation history for peer participation"],"best_for":["large-scale peer networks requiring quality assurance without central oversight","projects seeking to monetize peer contributions via token systems","applications where inference quality is critical and malicious peers must be identified"],"limitations":["Reputation systems are vulnerable to gaming; peers can collude to artificially boost each other's scores","Reputation recovery is slow; a peer with temporary issues may be deprioritized for extended periods","Incentive mechanisms require external token/reward system; not built-in to Petals core","Reputation scores are subjective and may not correlate with actual inference quality for all use cases"],"requires":["Reputation database (centralized or distributed)","Inference quality verification mechanism (e.g., comparing outputs across peers)","Optional: blockchain or token system for reward distribution"],"input_types":["inference results (tokens, latency, success/failure)","peer metadata (availability, response time)","user feedback (quality ratings, error reports)"],"output_types":["reputation scores (per peer)","reward distributions (if using token system)","peer rankings (for selection in load balancing)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-petals__cap_7","uri":"capability://automation.workflow.fault.tolerance.and.inference.retry.with.fallback.peers","name":"fault tolerance and inference retry with fallback peers","description":"Detects inference failures (peer disconnection, timeout, corrupted output) and automatically retries with alternative peers. Implements exponential backoff for retries to avoid overwhelming peers. Maintains fallback peer lists for each layer, allowing seamless failover if primary peer becomes unavailable. Includes timeout detection and circuit breaker pattern to quickly identify failing peers and remove them from rotation.","intents":["Ensure inference completes even when some peers fail or become unavailable","Automatically recover from transient network issues without user intervention","Identify permanently failing peers and stop routing to them","Provide transparent failover without requiring client-side retry logic"],"best_for":["applications requiring high reliability (>99% inference success rate)","networks with unstable peers or high churn","use cases where inference failure is unacceptable"],"limitations":["Retries add latency (exponential backoff can add 1-10 seconds for multiple failures)","Fallback peers may be slower or lower quality than primary peers; retried inferences may be slower","Circuit breaker pattern may incorrectly mark healthy peers as failing if they experience temporary slowdowns","No guarantee of inference completion if all peers for a layer become unavailable"],"requires":["Multiple peers per layer (minimum 2-3 for meaningful redundancy)","Timeout configuration (typically 5-30 seconds depending on network conditions)","Fallback peer list maintenance"],"input_types":["inference requests with timeout parameters","peer failure signals (timeout, connection error, invalid output)","retry configuration (max retries, backoff strategy)"],"output_types":["inference results (from successful peer)","retry metadata (number of retries, peers attempted)","failure logs (for debugging)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-petals__cap_8","uri":"capability://safety.moderation.model.weight.verification.and.integrity.checking","name":"model weight verification and integrity checking","description":"Verifies model layer integrity using cryptographic hashing (SHA-256) to detect corrupted or tampered weights. Implements content-addressable storage where layers are identified by their hash, enabling deduplication and integrity verification across peers. Includes optional signature verification for layers signed by model authors, preventing unauthorized modifications. Detects bit-flip errors and network corruption during layer transfer.","intents":["Detect corrupted model weights that would produce incorrect inference results","Prevent malicious peers from serving modified weights","Verify that downloaded layers match expected model versions","Enable deduplication of identical layers across different models"],"best_for":["applications where inference correctness is critical (medical, financial)","networks with untrusted peers where weight tampering is a concern","deployments requiring audit trails of model versions used"],"limitations":["Hash verification adds computational overhead (~1-5% per inference)","Signature verification requires public key infrastructure; adds complexity for key management","Hash mismatches don't indicate whether corruption is accidental or malicious; requires manual investigation","No protection against attacks where peers serve correct weights but modify them mid-inference"],"requires":["Cryptographic hash function (SHA-256 or similar)","Optional: public key infrastructure for signature verification","Layer metadata including expected hashes"],"input_types":["model layer data (binary weights)","expected layer hashes","optional: digital signatures"],"output_types":["verification results (pass/fail)","hash mismatches (if corruption detected)","integrity metadata"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-petals__cap_9","uri":"capability://automation.workflow.bandwidth.aware.layer.scheduling.and.batching","name":"bandwidth-aware layer scheduling and batching","description":"Schedules layer transfers based on available bandwidth to minimize total inference time. Batches multiple inference requests to amortize network overhead and improve GPU utilization. Implements request queuing with priority scheduling (e.g., shorter sequences prioritized over longer ones). Predicts layer transfer time based on size and available bandwidth, allowing clients to make informed decisions about request batching.","intents":["Maximize throughput by batching multiple inference requests together","Minimize latency for high-priority requests by prioritizing them in the queue","Reduce network overhead by scheduling layer transfers efficiently","Predict inference time to help clients decide whether to batch requests"],"best_for":["batch inference workloads (e.g., processing multiple documents)","applications with variable request priorities","scenarios where throughput is more important than individual request latency"],"limitations":["Batching increases latency for individual requests; not suitable for latency-critical applications","Priority scheduling may starve low-priority requests if high-priority requests arrive continuously","Bandwidth prediction is approximate; actual transfer time may vary based on network conditions","Batching requires buffering requests; adds memory overhead and complexity"],"requires":["Request queue with configurable batch size","Bandwidth estimation mechanism","Priority scheduling logic"],"input_types":["inference requests (with optional priority)","batch size parameters","bandwidth estimates"],"output_types":["batched inference results","latency metrics (per request and per batch)","throughput metrics (requests/second)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.9+ with CUDA support (for GPU acceleration)","Stable internet connection with sufficient bandwidth (minimum 10 Mbps recommended)","GPU with at least 1GB VRAM for meaningful participation as a peer","Access to Petals network (requires joining DHT or connecting to bootstrap nodes)","Active peer registry with at least 2-3 peers per model layer for redundancy","Network connectivity allowing bidirectional communication between client and all peers","Periodic health check mechanism (typically 10-30 second intervals)","Python 3.8+ or Node.js 14+ (depending on client library)","Petals network access (bootstrap node or peer list)"],"failure_modes":["Network latency between peers adds 50-200ms per forward pass depending on peer distance and bandwidth","Requires minimum GPU VRAM to hold at least one model layer; very small GPUs (<2GB) may not participate effectively","No built-in fault tolerance for peer disconnections mid-inference; requires client-side retry logic","Inference speed degrades with network congestion; not suitable for real-time applications requiring <100ms latency","Peer availability is non-deterministic; inference may fail if peers holding required layers go offline","Routing decisions are made per-inference and don't account for future peer state changes; may select suboptimal peers if network conditions change mid-inference","Health checks add overhead (~5-10ms per peer per check interval); frequent checks improve accuracy but increase network traffic","No global optimization across concurrent inferences; each request independently selects peers, potentially causing contention","Bandwidth estimation via probes is approximate and may not reflect actual throughput under load","Client libraries add abstraction overhead; advanced users may need lower-level APIs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.046Z","last_scraped_at":"2026-05-03T14:00:23.056Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=petals","compare_url":"https://unfragile.ai/compare?artifact=petals"}},"signature":"QCkqzCZa6/dWj/3Rs5pNmpQgyKFaIO4BjBOodwAL6h0no4tHlEY5LfcPJHCBzBDqCbeDAsmm5pwIxyCIkH6CCQ==","signedAt":"2026-06-21T02:22:15.328Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/petals","artifact":"https://unfragile.ai/petals","verify":"https://unfragile.ai/api/v1/verify?slug=petals","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}