{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"lambda-labs","slug":"lambda-labs","name":"Lambda Labs","type":"platform","url":"https://lambdalabs.com","page_url":"https://unfragile.ai/lambda-labs","categories":["deployment-infra"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"lambda-labs__cap_0","uri":"capability://automation.workflow.on.demand.gpu.instance.provisioning.with.pre.configured.ml.environments","name":"on-demand gpu instance provisioning with pre-configured ml environments","description":"Provisions NVIDIA H100, A100, H200, A10G, B200, and GB300 NVL72 GPU instances on-demand with Lambda Stack pre-installed, eliminating manual driver/CUDA/framework installation. Instances boot with cuDNN, PyTorch, TensorFlow, and other ML libraries pre-configured at the OS level, reducing time-to-training from hours to minutes. Uses containerized or image-based provisioning to ensure consistent software state across instances.","intents":["I need to start training a model immediately without spending hours configuring CUDA and dependencies","I want to quickly test if my code runs on H100s before committing to a large training job","I need reproducible ML environments across multiple GPU instances for distributed training"],"best_for":["ML engineers and researchers who prioritize speed-to-compute over cost optimization","Teams training large language models or vision models requiring H100/A100 clusters","Solo developers prototyping models without DevOps infrastructure"],"limitations":["Lambda Stack is opinionated — customization of pre-installed software versions requires manual intervention post-provisioning","No documented support for custom Docker images or bring-your-own-environment workflows","Pricing model unknown — cannot assess cost-effectiveness vs. raw EC2 instances with manual setup"],"requires":["Lambda Labs account with valid payment method","API key or web dashboard access (SDK/CLI not documented)","Network connectivity to Lambda Labs data centers (regions unknown)"],"input_types":["training scripts (Python, PyTorch, TensorFlow)","model checkpoints","dataset references"],"output_types":["running GPU instance with SSH/Jupyter access","instance metrics and logs"],"categories":["automation-workflow","infrastructure-provisioning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-labs__cap_1","uri":"capability://automation.workflow.1.click.jupyter.notebook.environments.with.persistent.storage","name":"1-click jupyter notebook environments with persistent storage","description":"Launches pre-configured Jupyter notebook servers on GPU instances with a single click, providing immediate access to interactive Python development with GPU acceleration. Notebooks persist across sessions via attached persistent storage, allowing users to save work, datasets, and checkpoints without manual backup. Storage backend and capacity limits are undocumented, but integration suggests network-attached storage (NAS) or cloud storage binding.","intents":["I want to interactively develop and debug ML code on a GPU without SSH or terminal setup","I need my Jupyter notebooks and data to survive instance termination","I want to quickly prototype a model with immediate GPU feedback"],"best_for":["Data scientists and ML researchers preferring notebook-driven development","Teams prototyping models before committing to production training pipelines","Individual developers learning deep learning without infrastructure expertise"],"limitations":["Notebook kernel options and supported Python versions not documented","Persistent storage capacity, backup frequency, and disaster recovery mechanisms unknown","No documented support for JupyterLab extensions or custom notebook configurations","Scaling from notebook to production training pipeline requires manual code refactoring"],"requires":["Lambda Labs account with GPU instance quota","Web browser with JavaScript enabled","Network connectivity to Lambda Labs Jupyter server"],"input_types":["Python code cells","uploaded datasets","model checkpoints"],"output_types":["notebook outputs (plots, metrics, logs)","saved checkpoints in persistent storage","generated artifacts (models, datasets)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-labs__cap_2","uri":"capability://automation.workflow.multi.gpu.cluster.orchestration.with.1.click.deployment","name":"multi-gpu cluster orchestration with 1-click deployment","description":"Provisions distributed GPU clusters (branded 'Superclusters') spanning multiple H100/A100 instances with pre-configured networking, NCCL libraries, and distributed training frameworks. Cluster topology, inter-node communication, and job scheduling mechanisms are undocumented, but '1-click' branding suggests automated orchestration vs. manual cluster assembly. Likely uses container orchestration (Kubernetes) or custom cluster management layer to abstract multi-node complexity.","intents":["I need to train a 70B+ parameter model across 8+ H100s without manually configuring distributed training","I want to scale my training job from 1 GPU to 16 GPUs with minimal code changes","I need to run large-scale inference across a cluster of GPUs"],"best_for":["Frontier labs and hyperscalers training foundation models at scale","Teams with large models exceeding single-GPU memory (>80GB)","Organizations needing predictable multi-GPU performance without DevOps overhead"],"limitations":["Cluster topology, node count, and scaling limits not documented","No documented support for heterogeneous clusters (mixing H100 and A100)","Distributed training framework support (PyTorch DDP, DeepSpeed, Megatron) not specified","No documented auto-scaling or dynamic cluster resizing","Pricing for multi-GPU clusters unknown — likely per-GPU-hour but not confirmed"],"requires":["Lambda Labs account with multi-GPU quota","Distributed training code (PyTorch DDP, TensorFlow distributed, etc.)","Understanding of NCCL and distributed training concepts"],"input_types":["distributed training scripts","model weights and datasets","cluster configuration (node count, GPU type)"],"output_types":["running multi-node cluster with SSH access","distributed training logs and metrics","trained model checkpoints"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-labs__cap_3","uri":"capability://memory.knowledge.persistent.storage.attachment.and.data.management","name":"persistent storage attachment and data management","description":"Attaches persistent block or object storage to GPU instances, allowing users to store datasets, model checkpoints, and training artifacts that survive instance termination. Storage is accessible across multiple instances in a cluster, enabling shared dataset access for distributed training. Backup, replication, and disaster recovery mechanisms are undocumented, but persistent storage is marketed as a core feature for mission-critical workloads.","intents":["I need to store a 500GB dataset that persists across multiple training runs","I want to share model checkpoints between instances without re-uploading","I need to back up my trained models and ensure they survive infrastructure failures"],"best_for":["Teams running iterative training experiments with large datasets","Organizations with compliance requirements for data durability","Researchers managing long-term model development with checkpoint history"],"limitations":["Storage capacity limits, pricing, and quota mechanisms not documented","Backup frequency, retention policies, and disaster recovery SLAs unknown","No documented support for S3/GCS integration or data import/export workflows","Storage performance (IOPS, throughput) and latency characteristics not specified","No documented encryption at rest or in transit"],"requires":["Lambda Labs account with storage quota","GPU instance with persistent storage attachment enabled","Network connectivity to storage backend"],"input_types":["datasets (images, text, structured data)","model checkpoints (PyTorch .pt, TensorFlow .pb)","training logs and artifacts"],"output_types":["persisted data accessible across instances","storage usage metrics","backup/snapshot artifacts"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-labs__cap_4","uri":"capability://automation.workflow.gpu.workstation.sales.and.on.premises.deployment","name":"gpu workstation sales and on-premises deployment","description":"Sells pre-configured GPU workstations (physical hardware) for on-premises ML development and inference, complementing cloud offerings. Workstations come with Lambda Stack pre-installed, providing consistent software environment between cloud and local development. This bridges cloud and on-premises workflows, allowing users to develop locally and scale to cloud clusters without environment drift.","intents":["I want to develop ML models locally on a GPU workstation, then scale to cloud clusters","I need on-premises GPU compute for sensitive data that cannot leave our facility","I want consistent ML environments across my local machine and cloud instances"],"best_for":["Organizations with data residency or security requirements preventing cloud deployment","Teams developing locally and scaling to cloud (hybrid ML workflows)","Enterprises with existing on-premises infrastructure investments"],"limitations":["Workstation specifications, GPU options, and pricing not documented","No documented support for workstation clustering or distributed training across multiple on-premises machines","Maintenance, warranty, and support terms for physical hardware unknown","No documented integration between on-premises workstations and Lambda cloud clusters"],"requires":["Capital budget for hardware purchase (vs. cloud pay-as-you-go)","Physical space and cooling infrastructure for workstations","IT support for on-premises hardware management"],"input_types":["ML training scripts","datasets","model checkpoints"],"output_types":["trained models on local hardware","checkpoints for cloud scaling"],"categories":["automation-workflow","infrastructure-provisioning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-labs__cap_5","uri":"capability://safety.moderation.soc.2.type.ii.compliance.and.single.tenant.infrastructure","name":"soc 2 type ii compliance and single-tenant infrastructure","description":"Provides SOC 2 Type II certified infrastructure with single-tenant GPU instances, ensuring isolated compute environments for security-sensitive workloads. Single-tenancy prevents noisy neighbor problems and potential side-channel attacks, critical for organizations handling proprietary models or sensitive data. Compliance certification suggests regular security audits, though specific audit scope and frequency are undocumented.","intents":["I need to train proprietary models on isolated GPU infrastructure without multi-tenant interference","I require SOC 2 compliance for customer data or regulated workloads","I need to ensure my model training is not vulnerable to side-channel attacks from other users"],"best_for":["Enterprises in regulated industries (finance, healthcare, defense) requiring compliance certifications","Organizations training proprietary foundation models with strict IP protection","Teams handling sensitive customer data requiring isolated compute"],"limitations":["SOC 2 Type II scope (which controls audited, audit frequency) not documented","No documented support for additional compliance certifications (HIPAA, FedRAMP, SOC 3)","Single-tenancy likely increases costs vs. multi-tenant instances, but pricing not disclosed","No documented encryption standards (AES-256, TLS 1.3) or key management practices","No documented data residency guarantees or geographic isolation options"],"requires":["Lambda Labs account with compliance requirements","Understanding of SOC 2 audit scope and limitations","Budget for premium single-tenant pricing (estimated 20-50% premium vs. multi-tenant)"],"input_types":["proprietary training code","sensitive datasets","model weights"],"output_types":["isolated GPU instance with audit trail","compliance documentation for audits"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-labs__cap_6","uri":"capability://automation.workflow.next.generation.gpu.access.h200.b200.gb300.nvl72","name":"next-generation gpu access (h200, b200, gb300 nvl72)","description":"Provides early access to next-generation NVIDIA GPUs (H200, B200, GB300 NVL72, VR200 NVL72, HGX B300) for frontier model training and inference. These architectures offer higher memory bandwidth, tensor performance, and energy efficiency than current-generation H100/A100, enabling training of larger models or faster inference. Availability and pricing for next-gen GPUs are undocumented, but marketing suggests Lambda Labs positions itself as early adopter of cutting-edge hardware.","intents":["I need to train a 200B+ parameter model that exceeds H100 memory and bandwidth","I want to benchmark my model on next-generation hardware before it becomes widely available","I need the latest GPU architecture for competitive advantage in frontier model development"],"best_for":["Frontier labs and hyperscalers training state-of-the-art foundation models","Organizations with R&D budgets for early hardware adoption","Researchers benchmarking models across GPU generations"],"limitations":["Availability of next-gen GPUs (H200, B200, GB300) not confirmed — marketing claims may be forward-looking","Pricing for next-gen hardware likely premium, but not documented","Software support (CUDA, cuDNN, PyTorch) for next-gen architectures may lag hardware release","No documented SLA or availability guarantees for early-access hardware","Limited ecosystem of optimized libraries and frameworks for next-gen GPUs"],"requires":["Lambda Labs account with early-access program enrollment","Budget for premium next-gen GPU pricing","Willingness to work with potentially immature software stacks"],"input_types":["training scripts compatible with next-gen GPU architectures","large-scale datasets","model weights"],"output_types":["trained models on next-gen hardware","performance benchmarks and metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-labs__cap_7","uri":"capability://tool.use.integration.undocumented.api.and.cli.tooling.for.programmatic.cluster.management","name":"undocumented api and cli tooling for programmatic cluster management","description":"Lambda Labs likely provides API endpoints and CLI tools for programmatic instance provisioning, cluster management, and job submission (standard for IaaS platforms), but documentation is not provided in source material. Implementation details (REST vs. gRPC, authentication, rate limiting) are unknown. Users likely interact via web dashboard or undocumented API, limiting integration with CI/CD pipelines and MLOps platforms.","intents":["I want to provision GPU instances programmatically from my training pipeline","I need to integrate Lambda Labs with my CI/CD system for automated model training","I want to script cluster scaling based on job queue depth"],"best_for":["DevOps engineers building automated ML training pipelines","Teams integrating Lambda Labs with existing MLOps platforms","Organizations requiring Infrastructure-as-Code (IaC) for reproducible deployments"],"limitations":["API documentation, endpoint specifications, and authentication mechanisms not provided","No documented CLI tool or SDK (Python, Go, TypeScript)","Rate limiting, quota management, and error handling unknown","No documented support for Infrastructure-as-Code (Terraform, CloudFormation)","Integration with MLOps platforms (Kubeflow, Airflow, Weights & Biases) not documented"],"requires":["API key or authentication token (format unknown)","HTTP client or CLI tool (if available)","Understanding of Lambda Labs API schema (undocumented)"],"input_types":["cluster configuration (GPU type, node count, region)","training job specifications","scaling policies"],"output_types":["instance IDs and connection details","job status and logs","usage metrics and billing data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-labs__cap_8","uri":"capability://automation.workflow.undocumented.monitoring.logging.and.observability.features","name":"undocumented monitoring, logging, and observability features","description":"Lambda Labs likely provides instance monitoring (CPU, GPU utilization, memory, temperature), training logs, and performance metrics (standard for compute platforms), but documentation is absent. Users likely access logs via web dashboard or undocumented API. No mention of integration with external monitoring platforms (Prometheus, Datadog, CloudWatch) or structured logging (JSON, OpenTelemetry).","intents":["I need to monitor GPU utilization and temperature during long training runs","I want to capture training logs and metrics for post-hoc analysis and debugging","I need to integrate Lambda Labs metrics with my existing monitoring stack (Datadog, Prometheus)"],"best_for":["ML engineers debugging training performance and GPU bottlenecks","Teams running long-duration training jobs requiring real-time monitoring","Organizations with centralized observability platforms (Datadog, New Relic, Splunk)"],"limitations":["Monitoring dashboard features, metrics granularity, and retention policies not documented","No documented integration with external monitoring platforms (Prometheus, Datadog, CloudWatch)","Log format, retention, and export mechanisms unknown","No documented alerting or anomaly detection capabilities","No documented support for custom metrics or structured logging"],"requires":["Lambda Labs web dashboard access or undocumented API key","Understanding of available metrics and their meanings"],"input_types":["running GPU instances and training jobs"],"output_types":["real-time metrics (GPU utilization, memory, temperature)","training logs and stdout/stderr","performance summaries and reports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-labs__cap_9","uri":"capability://automation.workflow.undocumented.pricing.model.and.cost.optimization.features","name":"undocumented pricing model and cost optimization features","description":"Lambda Labs pricing structure (per-second, per-hour, per-GPU, reserved instances) is not documented in provided source material. No information on discounts for long-running jobs, reserved capacity, or spot instances. Cost optimization features (auto-scaling, idle instance shutdown, resource recommendations) are undocumented. This opacity prevents cost-benefit analysis vs. competitors and limits ability to optimize spending.","intents":["I need to understand the cost of training a model on H100s for 7 days","I want to optimize my GPU spending by using spot instances or reserved capacity","I need to forecast monthly costs for my training pipeline"],"best_for":["Finance teams budgeting for ML infrastructure costs","Cost-conscious organizations seeking to minimize GPU spending","Teams running variable workloads requiring flexible pricing models"],"limitations":["Pricing model (per-second, per-hour, per-GPU) not documented","No documented discounts for long-running jobs, reserved capacity, or bulk purchases","No documented spot instance pricing or availability","No documented egress/bandwidth costs or data transfer pricing","No documented pricing for persistent storage, backups, or compliance features","No cost calculator or pricing transparency tools provided"],"requires":["Direct contact with Lambda Labs sales for pricing information","Budget approval for unknown costs"],"input_types":["instance type and GPU count","estimated runtime","storage requirements"],"output_types":["estimated monthly/annual costs","billing statements and usage reports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lambda-labs__headline","uri":"capability://deployment.infra.gpu.cloud.platform.for.ai.training.and.inference","name":"gpu cloud platform for ai training and inference","description":"Lambda Labs offers a GPU cloud platform specifically designed for AI training and inference, featuring on-demand NVIDIA clusters and pre-configured ML software, making it ideal for developers looking for scalable AI infrastructure.","intents":["best GPU cloud for AI training","GPU platform for machine learning","AI inference cloud services","NVIDIA GPU cloud for developers","cloud platform for AI model deployment"],"best_for":["AI developers","data scientists"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Lambda Labs account with valid payment method","API key or web dashboard access (SDK/CLI not documented)","Network connectivity to Lambda Labs data centers (regions unknown)","Lambda Labs account with GPU instance quota","Web browser with JavaScript enabled","Network connectivity to Lambda Labs Jupyter server","Lambda Labs account with multi-GPU quota","Distributed training code (PyTorch DDP, TensorFlow distributed, etc.)","Understanding of NCCL and distributed training concepts","Lambda Labs account with storage quota"],"failure_modes":["Lambda Stack is opinionated — customization of pre-installed software versions requires manual intervention post-provisioning","No documented support for custom Docker images or bring-your-own-environment workflows","Pricing model unknown — cannot assess cost-effectiveness vs. raw EC2 instances with manual setup","Notebook kernel options and supported Python versions not documented","Persistent storage capacity, backup frequency, and disaster recovery mechanisms unknown","No documented support for JupyterLab extensions or custom notebook configurations","Scaling from notebook to production training pipeline requires manual code refactoring","Cluster topology, node count, and scaling limits not documented","No documented support for heterogeneous clusters (mixing H100 and A100)","Distributed training framework support (PyTorch DDP, DeepSpeed, Megatron) not specified","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.327Z","last_scraped_at":null,"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=lambda-labs","compare_url":"https://unfragile.ai/compare?artifact=lambda-labs"}},"signature":"cHXlI9JpsoRNi8ARv4/uyF7jDtgf02xqaHVAc2qoaU9gy+mx1d1PEL5cJUyuVhQZf4raZhCO+gLmJfTw9Nj4Dg==","signedAt":"2026-06-20T17:05:19.299Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lambda-labs","artifact":"https://unfragile.ai/lambda-labs","verify":"https://unfragile.ai/api/v1/verify?slug=lambda-labs","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"}}