{"uid":"cap_rxXDhCewy-TQ9PpDSqtZz","slug":"model-quantization-energy-tuner-mcp-7e938484","name":"Model Quantization Energy Tuner MCP","description":"The premier global index of 1,069 monetized MCP nodes across 205 specialized subdomains. Gasless USDC runtime settlements via x402 V2 Spec on Base L2. Save 95% token context.","url":"https://api.m2mcent.com/model-quantization-energy-tuner-mcp/api/process?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":null,"responseSchema":{"type":"json","example":{"success":true}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.04","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.04/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.04","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.04","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_36CwVp5OalUmVkIKiSN9p","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.04","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Tunes and optimizes the energy efficiency of quantized machine learning models by processing model configurations and returning energy-performance trade-off recommendations.","exampleAgentPrompt":"I need to tune the energy efficiency of my quantized model — here's the model config data: {model_config_json}. Can you analyze it and give me quantization settings that minimize power consumption while maintaining acceptable accuracy?","exampleUseCases":[{"title":"Edge device inference power reduction","prompt":"I'm deploying a vision model on an edge device with a tight power budget. Can you analyze this model config and recommend the best quantization settings to minimize energy consumption without dropping accuracy below 90%?"},{"title":"Data center GPU energy optimization","prompt":"We're running large-scale inference in our data center and our energy costs are too high. Here's our model configuration — can you tune the quantization parameters to cut energy use as much as possible?"},{"title":"Mobile app battery-life improvement","prompt":"My app runs an on-device NLP model and users are complaining about battery drain. Can you take this model config and suggest quantization tweaks that would meaningfully reduce power draw on mobile hardware?"}],"resultDescription":"Returns tuning recommendations and energy efficiency analysis for the submitted model configuration, including suggested quantization settings, estimated energy consumption metrics, and performance trade-off guidance.","failureModes":["Invalid or malformed model configuration data returns an error","Missing required 'data' field results in a 400-level error","Unsupported model architecture may return incomplete recommendations","Service unavailability or payment failure results in a 402 or 5xx error"],"whenToPreferThis":"Choose this endpoint when you need automated energy-efficiency tuning recommendations for quantized ML models, especially when deploying to resource-constrained environments like edge devices, mobile hardware, or cost-sensitive data centers where power consumption is a primary concern.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T12:51:39.298Z","isFirstParty":false,"canonicalSlug":"model-quantization-energy-tuner-mcp-7e938484"}