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Spotlight
PAGE: https://alison.com/course/fundamentals-of-prometheus-software-performance-monitoring-for-it-systems
PROMISE: Learn to monitor CPU and system performance for background model hosting
WHO: IT operators and system admins managing servers that host background decision models
WHYNOW: Monitoring requirements grow as more workloads shift to hybrid CPU and NPU environments
DESC: Learn the fundamentals of Prometheus software for system monitoring by enrolling in this basic free online course.
BEFORE: CPU performance is monitored manually without automated alerts
BEFORE: NPU offloading effects are guessed rather than measured
BEFORE: Monitoring tooling choices are made without scenario fit assessment
AFTER: System performance is tracked with defined collection and storage rules
AFTER: Resource usage is described and verified through structured monitoring
AFTER: Tool selection is guided by clear beneficial and non-beneficial scenarios
GAIN: Clear understanding of how data is collected and stored for performance checks
GAIN: Ability to describe and verify monitoring setups for mixed CPU and NPU hosts
GAIN: Confidence in choosing Prometheus or alternatives based on use case fit
STEP: CPU usage spikes when decision models run in the background || Discuss the data collection and storage in Prometheus. || Verify if current hardware metrics support background load without manual logs
STEP: No visibility into whether NPU offloading is actually reducing CPU strain || Describe monitoring using Prometheus. || Set up metric queries to compare CPU and NPU utilization during model runs
STEP: Unsure which monitoring tools fit a hybrid CPU and NPU setup || Identify scenarios where Prometheus is beneficial and those where it is not. || Choose the right monitoring layer based on workload type and resource limits
STEP: Docker deployment of monitoring agents fails on host systems || Recognize the installation method of Prometheus using precompiled binaries and Docker images. || Deploy monitoring agents using the method that matches the host environment
NOTFOR: Teams needing advanced predictive analytics or real-time AI model debugging
NOTFOR: Readers without basic knowledge of system performance metrics
FACT: Average duration: 1.5-3 Hours
FACT: CPD-accredited certificate available
FACT: Intermediate level
COVERS: Discuss the data collection and storage in Prometheus.
COVERS: List the main features of Prometheus.
COVERS: Identify scenarios where Prometheus is beneficial and those where it is not.
COVERS: Describe monitoring using Prometheus.
COVERS: Distinguish between use cases for Prometheus and Grafana when deployed in collaboration.
COVERS: Recognize the installation method of Prometheus using precompiled binaries and Docker images.
COVERS: Discuss the four core metrics of Prometheus.
COVERS: Operating Systems
COVERS: Information Systems
COVERS: Docker
COVERS: DevOps
MODULE: Building Foundations in Prometheus || Introduction to Prometheus | Servers and Metrics in Prometheus | Prometheus and Grafana | Installing a Prometheus Environment
What you are probably seeing
Your system feels sluggish or lags when you switch to other applications while a local model is active in the background. Task Manager shows sustained high CPU usage that prevents other critical processes from getting enough resources, leading to dropped frames or slow response times.
You notice that even small, open-source models create a constant demand for processing power that exceeds what a standard laptop or desktop CPU can comfortably manage during routine daily tasks.
Why it happens
Central Processing Units are optimized for bursty, complex calculations rather than the low-latency, continuous inference required for background decision-making. When a model runs permanently, the CPU spends too much time on matrix operations, reducing efficiency and draining battery life on mobile devices.
Neural Processing Units are specialized hardware designed for neural network workloads. They handle these repetitive mathematical operations with significantly lower energy consumption and faster throughput, allowing the main CPU to remain idle and responsive for general user tasks.
What changed recently
2026-10-07 · Strands Decider 2B: a small, open-source, decision model
Strands is now offloading processing to the NPU for that gap:
• Strands Decider 2B is a small, open-source decision model.
• CPU performance is insufficient for background processing of this model.
Source: Hacker News discussion
Step by step
1. Identify the specific background processes causing high CPU load using your operating system’s task manager or resource monitor. Verify if your hardware includes a dedicated NPU by checking the system specifications or BIOS information. Download a benchmarking tool to measure the current CPU performance against the minimum requirements of the model you intend to run. Configure your model deployment settings to target the NPU instead of the CPU to offload the inference workload. Monitor the system for several days after the change to ensure background tasks no longer interfere with your primary workflows.
Ways to go further
Start here: Fundamentals of Prometheus Software – Performance Monitoring for IT Systems For when you need to monitor the performance of the systems hosting these models. Open the free course on Alison ↗
Better fit: Introduction to Decision Making For when you are new to the field and need general context before digging into specific tools. Open the free course on Alison ↗
Best when: The Basics of Decision Formulas For when you want to understand the logic behind the models you are running. Open the free course on Alison ↗
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