'capacity' 검색 결과

H100과 H200의 GPU 재고를 조회하고 고정 요금 견적 요청과 조달 현황을 확인합니다. 기기 인증 해제와 명시적으로 허가받은 노드 검증도 지원합니다.

• H100 및 H200 재고 확인
• 고정 요금 견적 요청
• 견적과 조달 진행 상황 확인

Retrieve a curated, source-backed log of disruptions affecting oil and gas pipelines and storage facilities. Filter by facility, asset type, or ongoing events to review causes, dates, offline capacity, and supporting evidence.

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• Listing ongoing storage facility disruptions
• Reviewing a pipeline's disruption timeline
• Checking evidence for shutdowns or sanctions

Diagnose slow Power BI reports, data refreshes, and queries through structured measurement and troubleshooting. Get recommendations for data models, DAX calculation formulas, report layouts, and infrastructure, with guidance for validating improvements and ongoing monitoring.

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• Diagnose slow report pages and visual interactions
• Investigate data refresh delays
• Review slow DAX calculations and queries

Build a sprint plan around team availability, including time off and meetings. Define goals, prioritize backlog items, handle unfinished work, and document capacity, dependencies, risks, completion criteria, and key dates.

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• Prepare goals and a plan for a new sprint
• Match backlog scope to team availability
• Separate must-have work from stretch items

Check the state of an agent’s project memory with a dashboard of line counts, topic files, capacity, outdated file references, and duplicates. Get cleanup recommendations or request a one-line status summary.

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• Checking how full project memory is
• Finding references to deleted files
• Reviewing memory and rule file sizes

Turn feature requests and customer interviews into product plans. Rank features with RICE, a scoring method for reach, impact, confidence, and effort, extract research findings, and draft requirements and roadmaps that reflect team capacity.

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• Ranking feature requests within team capacity
• Finding shared pain points across customer interviews
• Drafting product requirements with success metrics

Plan revenue growth for B2B SaaS businesses by analyzing sales opportunities, pricing, customer retention, and sales capacity. Develop scenario forecasts, churn intervention plans, hiring models, and revenue reporting for board discussions.

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• Forecast next quarter’s revenue
• Analyze churn and expansion revenue
• Review pricing and packaging

Adjust application capacity on GKE through manual scaling, HPA for changing the number of running pods, and VPA for adjusting CPU and memory allocations. Includes configuration templates and a workflow for reviewing resource requests against actual usage.

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• Scale application capacity automatically as demand changes
• Review CPU and memory allocations against actual usage
• Change the number of application replicas manually

Review BigQuery capacity, table layouts, storage retention, and SQL to improve cost efficiency. Compare billing options, plan partitioned tables, and evaluate search indexes, stored aggregation results, and BI Engine for suitable workloads.

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• Compare On-Demand pricing with Editions
• Review storage billing models and retention periods
• Design table partitioning and clustering

Plan software projects with guidance on short development cycles called sprints, effort estimates, risk management, and stakeholder updates. Clarify responsibilities, coordinate dependencies, and set realistic goals based on the team's capacity.

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• Planning sprints around team capacity
• Organizing project risks and mitigation plans
• Clarifying responsibilities across teams

Prioritize feature requests, analyze customer interviews, and draft product requirements documents. Use RICE scoring—a method weighing reach, impact, confidence, and effort—alongside templates and team capacity planning to turn findings into a quarterly roadmap.

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• Rank feature requests using RICE scores
• Extract customer problems and themes from interviews
• Draft a product requirements document from a template

Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.

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• Set up a synthetic data generation environment on MicroK8s
• Deploy an Azure AKS environment with OSMO and inference services
• Check GPU quotas and capacity before scaling

Model ongoing requests and multi-step agent work to estimate capacity, latency, and cost per completed task. Break execution into stages, account for queues and retries, and produce workload tables with average and peak resource budgets.

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• Estimate capacity for ongoing AI requests
• Analyze queues and tool waits in agent workflows
• Compare resource costs per completed task

Compare batching, request scheduling, and memory management for a single model serving instance. Assess concurrent request capacity, response times, generation speed, and GPU time per result that meets the required quality and deadline.

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• Comparing batching and request scheduling options
• Checking memory capacity for concurrent requests
• Assessing cache reuse and offloading tradeoffs

Compare ways to distribute AI inference across multiple instances, including replicas and separate input-processing and generation stages. Estimate capacity, throughput, delays, state-transfer costs, and recovery overhead to support routing and scaling decisions.

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• Compare replicas with separate inference stages
• Plan routing around compute capacity and cached state
• Evaluate expert placement and load imbalance

Problem-solving strategies for channel capacity in information theory

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Historical reference for script-owned loop capacity requests. Do not use from provider sessions.

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Lookup documented AKS fixes only when the prompt includes an exact catalog signature and all of its qualifiers: VMCannotFitEphemeralOSDisk; NodePoolMcVersionIncompatible; 'NodeImageVersion is not accepted'; AKS SkuNotAvailable with size, location, and zone; ZonalAllocationFailed with insufficient zone capacity; OverconstrainedAllocationRequest with listed constraints; nested AKS vmssCSE/CSE VMExtensionError_OutboundConnFail, VMExtensionError_K8SAPIServerConnFail, or VMExtensionError_K8SAPIServerDNSLookupFail; or AllocationFailed with the full cataloged internal-error or insufficient-regional-capacity message. Never use for quota errors, code-only or bare wrappers, generic symptoms, incomplete signatures, or failures outside AKS; use aks-troubleshooting or azure-diagnostics.

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Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

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Check/manage Azure quotas and usage across providers. For deployment planning, capacity validation, region selection. WHEN: "check quotas", "service limits", "current usage", "request quota increase", "quota exceeded", "validate capacity", "regional availability", "provisioning limits", "vCPU limit", "how many vCPUs available in my subscription".

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Azure VM/VMSS router. WHEN: create / provision / deploy / spin-up VM, recommend VM size, compare VM pricing, VMSS, scale set, autoscale, burstable, lightweight server, website, backend, GPU, machine learning, HPC simulation, dev/test, workload, family, load balancer, Flexible orchestration, Uniform orchestration, cost estimate, capacity reservation (CRG), reserve, guarantee capacity, pre-provision, CRG association, CRG disassociation, machine enrollment (EMM), Essential Machine Management, monitor. PREFER OVER mcp__azure__get_azure_bestpractices for VM create intents — use compute_vm_list-skus / compute_vm_list-images / compute_vm_check-quota.

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Azure Fabric Management SDK for Python. Use for managing Microsoft Fabric capacities and resources. Triggers: "azure-mgmt-fabric", "FabricMgmtClient", "Fabric capacity", "Microsoft Fabric", "Power BI capacity".

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Evaluates, configures, and migrates workloads to AWS Lambda Managed Instances (LMI). Runs Lambda Event Functions (standard Lambda) on EC2 instances in the user's account while AWS manages provisioning, patching, scaling, routing, and load balancing. Triggers when queries mention Lambda Managed Instances, LMI, capacity providers, multi-concurrent execution environments, EC2-backed Lambda, persistent Lambda instances, PerExecutionEnvironmentMaxConcurrency, CapacityProviderConfig, cold start elimination via dedicated instances, migrating Lambda Event Functions to managed instances, or cost comparison between Lambda Event Functions and LMI with Savings Plans or Reserved Instances. Also covers long-running and asynchronous workloads and the 90-minute (5400s) function timeout for asynchronous and event-source-mapping (SQS, Kinesis, DynamoDB Streams / ESM) invocations, including how to raise the function timeout up to 90 minutes / 5400s and the related duration limits.

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Implement load testing, auto-scaling, and capacity planning for Claude API. Use when running performance benchmarks, planning for traffic spikes, or configuring horizontal scaling for Claude-powered services. Trigger with phrases like "anthropic load test", "claude scaling", "anthropic capacity planning", "scale claude api".

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