{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_gpux-ai","slug":"gpux-ai","name":"GPUX.AI","type":"product","url":"https://gpux.ai","page_url":"https://unfragile.ai/gpux-ai","categories":["deployment-infra"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_gpux-ai__cap_0","uri":"capability://automation.workflow.sub.second.gpu.container.cold.start.with.persistent.warm.pools","name":"sub-second gpu container cold start with persistent warm pools","description":"Eliminates traditional serverless cold start latency (typically 5-30 seconds on Lambda) by maintaining a pool of pre-warmed GPU containers that are kept in a hot state and rapidly allocated to incoming inference requests. The architecture likely uses container image caching, GPU memory pre-allocation, and request routing to idle instances rather than spawning fresh containers on demand, achieving 1-second startup times for model inference workloads.","intents":["Deploy a custom LLM and serve inference requests with minimal latency for production use cases","Run time-sensitive inference tasks without paying for always-on GPU instances","Test model performance under realistic latency constraints before committing to dedicated infrastructure"],"best_for":["ML teams building latency-sensitive inference APIs","Indie developers monetizing models who can't afford dedicated GPU servers","Researchers benchmarking inference performance across model variants"],"limitations":["Warm pool sizing and cost trade-offs not publicly documented — unclear how many concurrent warm containers are maintained per user tier","1-second claim likely applies to already-loaded models; first deployment or model updates may incur longer initialization","No published SLA or uptime guarantees for production workloads","Scaling behavior under traffic spikes unknown — may revert to cold starts if warm pool exhausted"],"requires":["Model in supported format (ONNX, PyTorch, TensorFlow, or containerized format)","API key for GPUX.AI platform","Network connectivity to GPUX.AI inference endpoints"],"input_types":["containerized model images","model weights (PyTorch, TensorFlow, ONNX)","inference request payloads (JSON, binary)"],"output_types":["inference results (JSON, binary)","structured predictions","streaming responses"],"categories":["automation-workflow","infrastructure-as-a-service"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gpux-ai__cap_1","uri":"capability://automation.workflow.model.monetization.and.revenue.sharing.marketplace","name":"model monetization and revenue-sharing marketplace","description":"Provides a built-in mechanism for model creators to list custom or fine-tuned models on a marketplace where other developers can invoke them via API, with automatic revenue splitting between the platform and the model creator. The system handles billing, usage tracking, and payout distribution without requiring creators to build their own payment infrastructure, likely using metered API calls as the billing unit and a percentage-based revenue split model.","intents":["Monetize a custom fine-tuned model without building billing infrastructure or managing customer payments","Discover and use specialized models from other creators without deploying infrastructure","Generate passive income from a trained model by listing it once and letting others pay per API call"],"best_for":["Independent ML researchers and practitioners with specialized models","Small teams lacking payment processing and billing infrastructure","Model creators seeking low-friction commercialization without SaaS overhead"],"limitations":["Revenue split percentage not publicly disclosed — unclear if creators receive 50%, 70%, or other split","No transparency on minimum payout thresholds or payment frequency (weekly, monthly, etc.)","Marketplace discovery and ranking algorithm unknown — unclear how models gain visibility vs competing offerings","No built-in usage analytics or detailed per-customer billing visibility for model creators","Potential vendor lock-in — models deployed on GPUX.AI cannot be easily migrated to other platforms"],"requires":["Trained model in supported format","GPUX.AI account with verified identity for payout eligibility","Model must comply with platform's acceptable use policy","API endpoint configuration and model metadata (name, description, pricing)"],"input_types":["model metadata (name, description, category, pricing tier)","model weights and configuration","usage terms and licensing information"],"output_types":["marketplace listing URL","API endpoint for model invocation","revenue reports and payout records"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gpux-ai__cap_2","uri":"capability://tool.use.integration.serverless.gpu.inference.api.with.multi.model.routing","name":"serverless gpu inference api with multi-model routing","description":"Exposes deployed models via REST/gRPC APIs with automatic request routing to available GPU instances, handling concurrent inference requests without requiring users to manage load balancing, auto-scaling, or GPU allocation. The platform abstracts away infrastructure complexity by providing a simple HTTP endpoint that accepts inference payloads and returns results, with built-in support for batching, streaming, and concurrent request handling across multiple GPU workers.","intents":["Call a deployed model via a simple HTTP API without managing GPU infrastructure or scaling","Handle variable inference load without provisioning dedicated GPU capacity","Integrate model inference into existing applications via standard REST endpoints"],"best_for":["Application developers integrating AI inference without ML infrastructure expertise","Teams with variable inference workloads that don't justify dedicated GPU servers","Rapid prototyping scenarios where infrastructure setup overhead should be minimized"],"limitations":["API latency includes network round-trip time in addition to model inference time — total latency likely 100-500ms depending on payload size and network conditions","No published rate limiting or quota documentation — unclear if there are per-user request limits or burst allowances","Batch inference capabilities not documented — unclear if platform supports efficient batching for throughput optimization","Streaming response support unknown — may require full response buffering before returning to client","No local inference option — all requests must traverse network to GPUX.AI infrastructure"],"requires":["GPUX.AI API key","Model deployed on GPUX.AI platform","Network connectivity to GPUX.AI API endpoints","HTTP client library (curl, requests, fetch, etc.)"],"input_types":["JSON payloads","binary data (images, audio)","structured inference parameters"],"output_types":["JSON responses","binary predictions","streaming response chunks"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gpux-ai__cap_3","uri":"capability://automation.workflow.freemium.gpu.access.tier.with.usage.based.upgrade.path","name":"freemium gpu access tier with usage-based upgrade path","description":"Provides free GPU compute access to users for experimentation and development, with transparent upgrade to paid tiers as usage scales. The freemium model likely includes limited GPU hours per month, reduced concurrency, or slower hardware (e.g., shared GPUs), with paid tiers offering higher quotas, dedicated resources, and priority scheduling. This removes friction for initial adoption while creating a natural monetization funnel as users' inference demands grow.","intents":["Experiment with model deployment and inference without upfront payment or credit card","Prototype an inference-based application to validate product-market fit before committing budget","Test GPUX.AI's performance and reliability before migrating production workloads"],"best_for":["Individual developers and researchers with limited budgets","Startups in early validation phases avoiding infrastructure costs","Teams evaluating GPUX.AI against competing platforms"],"limitations":["Freemium tier quotas and limits not publicly documented — unclear how many GPU hours, concurrent requests, or model deployments are allowed","Upgrade pricing and tier structure not transparent — difficult to forecast costs as usage scales","No published SLA or performance guarantees on free tier — may experience throttling or deprioritization","Free tier may have restrictions on model types, inference latency, or monetization eligibility","No clear path to estimate when free tier will be exhausted for a given workload"],"requires":["GPUX.AI account (email signup)","No credit card required for freemium tier","Compliance with platform's acceptable use policy"],"input_types":["model deployment requests","inference API calls"],"output_types":["usage reports","billing estimates","upgrade recommendations"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gpux-ai__cap_4","uri":"capability://automation.workflow.containerized.model.deployment.with.custom.runtime.support","name":"containerized model deployment with custom runtime support","description":"Accepts containerized models (Docker images) or model weights in standard formats (PyTorch, TensorFlow, ONNX) and deploys them to GPU infrastructure without requiring users to manage container orchestration, image building, or runtime configuration. The platform likely provides base images with common ML frameworks pre-installed, automatic dependency resolution, and support for custom entrypoints, enabling deployment of arbitrary model architectures and inference code.","intents":["Deploy a custom model with non-standard dependencies or inference logic without learning Kubernetes","Use a pre-built base image to avoid dependency management and focus on model code","Deploy models from multiple frameworks (PyTorch, TensorFlow, JAX, etc.) on the same platform"],"best_for":["ML engineers with custom model architectures or inference pipelines","Teams using multiple ML frameworks and needing unified deployment","Researchers deploying experimental models with non-standard dependencies"],"limitations":["Supported model formats and frameworks not fully documented — unclear if JAX, MLflow, or other formats are supported","Custom dependency installation process not documented — unclear if pip, conda, or apt-get are supported","Container image size limits not published — may reject large models or images","No version pinning or reproducibility guarantees — dependency resolution may differ between deployments","Debugging failed deployments likely requires platform-provided logs with limited visibility into build process"],"requires":["Model in supported format (PyTorch, TensorFlow, ONNX) or as Docker image","Model must fit within GPU memory constraints (typically 8-80GB depending on tier)","Inference code must expose HTTP endpoint or be compatible with platform's invocation protocol","GPUX.AI account and API key"],"input_types":["Docker image URIs","model weight files","inference code (Python, etc.)","dependency specifications (requirements.txt, environment.yml)"],"output_types":["deployed model endpoint URL","deployment status and logs","model metadata and configuration"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gpux-ai__cap_5","uri":"capability://data.processing.analysis.usage.based.metering.and.cost.tracking.for.inference.workloads","name":"usage-based metering and cost tracking for inference workloads","description":"Tracks inference API calls, GPU compute time, and data transfer, aggregating usage into billable units (likely per-request or per-GPU-second) and providing dashboards for cost visibility. The system likely meters requests at the API gateway level, correlates usage with specific models or users, and generates detailed usage reports showing cost breakdown by model, time period, or customer. This enables transparent cost attribution and helps users understand their inference spending patterns.","intents":["Monitor inference costs in real-time to avoid unexpected bills","Understand which models or customers are driving the highest costs","Forecast inference spending based on historical usage patterns"],"best_for":["Teams running multiple models and needing cost attribution per model","Marketplace creators tracking revenue from deployed models","Cost-conscious developers optimizing inference efficiency"],"limitations":["Metering granularity not documented — unclear if billing is per-request, per-second, or per-GPU-hour","Cost breakdown by component (compute, storage, network) not published — difficult to optimize specific cost drivers","No cost forecasting or budget alert features documented","Pricing for different GPU types (A100, H100, etc.) not transparent — unclear if costs vary by hardware","No published cost comparison vs alternatives (Lambda, Replicate, self-hosted) making ROI analysis difficult"],"requires":["GPUX.AI account with billing configured","Active inference workload generating API calls","Access to usage dashboard (likely web-based)"],"input_types":["inference API calls","model metadata (for cost attribution)"],"output_types":["usage reports (CSV, JSON)","cost breakdowns by model/time period","billing invoices"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gpux-ai__cap_6","uri":"capability://automation.workflow.model.versioning.and.a.b.testing.infrastructure","name":"model versioning and a/b testing infrastructure","description":"Supports deploying multiple versions of the same model and routing traffic between them for A/B testing, canary deployments, or gradual rollouts. The platform likely maintains version history, allows traffic splitting by percentage or user segment, and provides metrics to compare model performance across versions. This enables safe model updates and experimentation without downtime or requiring manual traffic management.","intents":["Deploy a new model version to a small percentage of traffic to validate performance before full rollout","Compare inference latency and accuracy between model versions in production","Rollback to a previous model version if a new deployment causes performance degradation"],"best_for":["Teams iterating on model improvements and needing safe deployment","ML practitioners running continuous A/B tests on model variants","Production systems requiring zero-downtime model updates"],"limitations":["Traffic splitting configuration not documented — unclear if split is by percentage, user ID, or other criteria","Metrics collection and comparison features not published — unclear what performance metrics are tracked","Version retention policy unknown — unclear how many versions are retained or if there are storage costs","Rollback mechanism not documented — unclear if rollback is instant or requires redeployment","No published guidance on minimum sample sizes for statistically significant A/B test results"],"requires":["Multiple model versions deployed on GPUX.AI","API key with permissions to configure traffic splitting","Monitoring/analytics integration to compare version performance"],"input_types":["model versions (as separate deployments)","traffic split configuration (percentages or rules)"],"output_types":["version metadata and deployment status","traffic split configuration","performance comparison metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gpux-ai__cap_7","uri":"capability://automation.workflow.automatic.model.optimization.and.quantization.for.inference","name":"automatic model optimization and quantization for inference","description":"Automatically applies optimization techniques (quantization, pruning, distillation, or graph optimization) to deployed models to reduce latency and memory usage without requiring manual configuration. The platform likely detects model architecture, applies framework-specific optimizations (e.g., TensorRT for NVIDIA, ONNX Runtime optimizations), and benchmarks optimized versions to ensure accuracy preservation. This enables faster inference and lower GPU memory requirements without user intervention.","intents":["Reduce model inference latency without manually tuning quantization parameters","Deploy larger models on smaller GPUs by automatically optimizing memory usage","Improve inference throughput for high-traffic models without code changes"],"best_for":["Teams deploying models without ML optimization expertise","Cost-sensitive deployments where reducing GPU requirements directly impacts budget","Latency-critical applications where automatic optimization can provide meaningful speedups"],"limitations":["Optimization techniques applied not documented — unclear if quantization, pruning, or other methods are used","Accuracy impact of optimizations not published — unclear if accuracy loss is measured or guaranteed to be below threshold","Opt-out mechanism not documented — unclear if users can disable optimizations for models where accuracy is critical","Optimization latency not published — unclear if optimizations are applied at deployment time (delaying deployment) or runtime","Framework support for optimizations unknown — may only work with specific frameworks (PyTorch, TensorFlow) or model architectures"],"requires":["Model in supported format (PyTorch, TensorFlow, ONNX)","Model must be compatible with platform's optimization pipeline","GPUX.AI account with optimization feature enabled"],"input_types":["model weights and architecture","optimization preferences (if configurable)"],"output_types":["optimized model","performance metrics (latency, memory, accuracy)","optimization report"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Model in supported format (ONNX, PyTorch, TensorFlow, or containerized format)","API key for GPUX.AI platform","Network connectivity to GPUX.AI inference endpoints","Trained model in supported format","GPUX.AI account with verified identity for payout eligibility","Model must comply with platform's acceptable use policy","API endpoint configuration and model metadata (name, description, pricing)","GPUX.AI API key","Model deployed on GPUX.AI platform","Network connectivity to GPUX.AI API endpoints"],"failure_modes":["Warm pool sizing and cost trade-offs not publicly documented — unclear how many concurrent warm containers are maintained per user tier","1-second claim likely applies to already-loaded models; first deployment or model updates may incur longer initialization","No published SLA or uptime guarantees for production workloads","Scaling behavior under traffic spikes unknown — may revert to cold starts if warm pool exhausted","Revenue split percentage not publicly disclosed — unclear if creators receive 50%, 70%, or other split","No transparency on minimum payout thresholds or payment frequency (weekly, monthly, etc.)","Marketplace discovery and ranking algorithm unknown — unclear how models gain visibility vs competing offerings","No built-in usage analytics or detailed per-customer billing visibility for model creators","Potential vendor lock-in — models deployed on GPUX.AI cannot be easily migrated to other platforms","API latency includes network round-trip time in addition to model inference time — total latency likely 100-500ms depending on payload size and network conditions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.893Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=gpux-ai","compare_url":"https://unfragile.ai/compare?artifact=gpux-ai"}},"signature":"mN9Xbxb2Wca4HLevAJvHRIby+2BKVZHrucLTO7PfdoN2ReYl6MQrCDkX0eTmbT/74r2wrOhyvPus6vMlJ2tTBA==","signedAt":"2026-06-21T13:44:39.892Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gpux-ai","artifact":"https://unfragile.ai/gpux-ai","verify":"https://unfragile.ai/api/v1/verify?slug=gpux-ai","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"}}