QuantMap: An LLM Inference Optimization and Telemetry Experiment Platform for Machine-Specific Tuning

QuantMap is an LLM inference optimization and telemetry experiment platform for machine-specific tuning. Its core philosophy is "benchmarking as forensic science"—emphasizing that every conclusion must be supported by evidence, anomalies are traceable, and comparisons consider statistical significance. It collects relational data between server parameters (number of threads, batch size, GPU layer offloading) and performance metrics through structured testing activities, providing monitored environments, evidence-bound reports, and persistent forensic records to help users shift from trial-and-error parameter tuning to data-driven optimization.

QuantMap's core philosophy is "Stop guessing your inference settings—measure them", treating benchmarking as forensic science. Its design embodies three principles: 1. Monitored environment (continuously records background interference); 2. Evidence-bound narrator (draws conclusions only when statistically marginally significant); 3. Persistent forensic records (fully traces every request, response, and thermal event). It also clarifies its positioning: it does not fix bad configurations, only provides suboptimal evidence; it does not make subjective rankings, but considers both performance and stability comprehensively.

QuantMap organizes benchmarking through "Campaigns" to scan server parameter spaces and collect telemetry data. The testing process includes setup checks (init/doctor/self-test), execution (run), and analysis (explain). Key CLI commands cover initialization, interference checks, self-tests, running campaigns, generating reports, etc. Methodologically, it strictly separates software updates (affecting UI/diagnostics, etc., without modifying raw data) from methodology updates (affecting conclusions, creating new interpretation layers) to ensure the comparability of historical results.

QuantMap collects multi-dimensional telemetry data: hardware status (GPU temperature/utilization, CPU/GPU utilization, memory, power consumption), performance metrics (token generation rate, first token time, batch throughput, end-to-end latency), and environmental interference (system updates, indexing services, other GPU applications, etc.). Trust mechanisms include: unrepairable raw data (data damaged by thermal throttling or interference cannot be repaired), identification of invalid comparisons (comparisons using different methodologies are marked as mismatched), and clear labeling of missing telemetry (e.g., marked as unknown if HWiNFO is not running).

The practical application value of QuantMap includes: parameter space exploration (finding the optimal configuration for specific hardware), performance regression detection (identifying changes by comparing historical data), hardware comparison (comparing different configurations under controlled variables), bottleneck identification (locating computation/memory/thermal bottlenecks), and evidence-driven decision-making (supporting infrastructure investment). Anomaly troubleshooting uses a five-command process: about (confirm tool identity), status (lab health), doctor (background interference), self-test (core logic verification), export (desensitized case files).

QuantMap is developed in phases: Phase1 (Trust Package), 1.1 (Stabilization), 2 (Operational Robustness), and 2.1 (Setup/Environment Bridging) have been completed; the current focus is Phase3 (Platform Generalization, ensuring clear and scalable architecture). Outlook: QuantMap represents a new paradigm for LLM inference benchmarking, shifting from casual testing to a forensic science approach, helping developers deploy more efficient and reliable AI services. Its slogan "Because guessing is not engineering" summarizes its core value.