Categories › Data & Databases › Vector databases & embeddings
GPU and LLM inference benchmarks, hardware evidence, deployment recommendations, and launch configs.
Remote server: your requests and data go to mcp.computesage.com. Claude Code: claude mcp add --transport http computesage-stackbench https://mcp.computesage.com/mcp · Claude app: Settings → Connectors → Add custom connector.
The tools this server listed when we connected to it, in its own words.
search_evidence — $0.00 free search of bounded public StackBench benchmark and failure evidence; use this for evidence-only requests.check_deployment_fit — $0.01 USDC on Base mainnet: check model/configuration fit against bounded StackBench capacity evidence.predict_performance — $0.05 USDC: predict configuration throughput, memory, and power from bounded StackBench evidence with confidence, missing metrics, and cavearecommend_deployment — $0.25 USDC: recommend what to buy or run from bounded StackBench fit, performance, confidence, evidence, caveats, and economics only when sugenerate_launch_config — $0.50 USDC: generate deterministic vLLM or llama.cpp launch templates from an exact StackBench recommendation without claiming a launch occuNot tested by KitScout yet. When our harness runs it, pros, cons and the tasks we tried appear here, with the date and evidence.