Introduction
This post shares a small, self-initiated tech experiment I did to explore how CPU-based proof-of-work systems actually perform in real-world conditions. It’s not a tutorial, recommendation, or speed test — just a learning-focused walkthrough of a short experiment run in a controlled local setup. The main goal is to understand system behavior, tradeoffs, and practical insights rather than chasing performance or production-level results.
Motivation
I’ve always been intrigued by how proof‑of‑work systems actually function beyond the whitepapers and theoretical discussions—especially in the context of real cryptocurrencies like Monero, which rely heavily on CPU‑based mining. I wanted to see what that process looks like up close: how hashing workloads behave, how much strain it puts on the CPU, and how performance, temperature, and system responsiveness shift under continuous load.
This wasn’t about making a profit or running a large‑scale mining setup. It was purely a short, self‑driven experiment to understand what happens at the system level when a CPU is asked to perform proof‑of‑work computations nonstop. I was curious about how my machine would handle it, what kind of thermal and power tradeoffs would show up, and how the performance scales with different thread counts or configurations.
By running this experiment locally with Monero mining, I learned much more than I expected—how small differences in setup affect performance, how resource management plays a big role, and how quickly the limits of consumer‑grade hardware can appear in a compute‑intensive context.
Setup
For this experiment, I used XMRig as the main tool to interface with the proof‑of‑work process. It offered a simple way to interact with the hashing algorithm, monitor performance, and observe system behavior without diving too deep into complex configurations.
I chose Monero specifically because of its RandomX algorithm, which is designed to be CPU‑friendly and resistant to ASIC dominance. That made it a perfect candidate for exploring how proof‑of‑work behaves on regular consumer hardware without needing specialized equipment.
The setup was intentionally lightweight and controlled. I ran short‑duration mining sessions rather than long continuous operations to get a snapshot of how my system handled the workload. I didn’t try to tune performance, tweak parameters, or aim for higher hash rates. The focus stayed entirely on observation—understanding how the CPU, memory, and thermals responded in real time under different conditions.
Proof‑of‑Work & RandomX
At its core, proof‑of‑work (PoW) is a mechanism that attaches a real computational cost to creating new blocks in a blockchain. It’s essentially a way to make adding data to the chain “expensive” in terms of computing effort, ensuring that participants have to invest real resources—like CPU time and electricity—to contribute honestly. The difficulty of this work keeps the network secure by making it impractical to manipulate the ledger.
Monero’s RandomX algorithm takes this concept a step further. It’s intentionally designed to be CPU‑friendly, allowing everyday computers to participate meaningfully without needing specialized mining hardware. The algorithm resists ASIC dominance by relying heavily on memory and cache behavior, making it difficult for hardware with fixed logic to gain a major efficiency advantage.
What I found most interesting is the contrast between the theory and the practical experience. On paper, proof‑of‑work looks like a simple hashing loop—but in practice, it translates into tangible system strain. As the CPU ramps up, you can actually feel the cost of computation: rising temperatures, higher power draw, and performance tradeoffs that make the “cost” of security very real on a physical level.
Observed System Behaviour
During the experiment, the system maintained a steady processing rate of just over 2.1 kH/s, which showed that the CPU could sustain a consistent hashing throughput under a fully CPU‑bound proof‑of‑work load. All available threads were engaged, making it easy to see how the operating system’s scheduler spreads continuous, high‑intensity tasks evenly across cores to keep things balanced.
As the test progressed, the CPU temperature quickly climbed and then leveled off between 85°C and 95°C. This steady thermal plateau suggested that heat—not computational limits—became the main bottleneck once the system reached equilibrium. Essentially, the processor was capable of doing more work, but the thermal constraints of laptop‑class hardware stepped in to cap performance and prevent overheating.
What stood out most was the clear tradeoff between sustained performance and thermal headroom. Even with stable throughput, continuous proof‑of‑work computation pushed the system near its thermal ceiling, emphasizing how physical design factors—cooling, power limits, and airflow—play just as crucial a role as raw compute capability in such workloads.
Limitations Of The Experiment
Despite being informative, this experiment had clear boundaries. It was conducted on a single machine over a short time frame, providing only a limited snapshot rather than a comprehensive dataset. There were no comparisons across algorithms, hardware types, or configuration settings, so any observed results apply only to this specific setup.
I also made no attempt at optimization or benchmarking, since the focus was on observation rather than performance measurement. Environmental factors—such as ambient temperature, background processes, and power settings—were not systematically controlled, which likely influenced the thermal readings and throughput consistency.
What I Learned
The biggest takeaway from this project was witnessing the difference between conceptual and operational proof‑of‑work. Reading about PoW gives you the logic; running it gives you the experience. You start to see how abstract security mechanisms translate into tangible system load, power draw, and thermal dynamics.
It also reinforced the importance of system‑level thinking. Proof‑of‑work isn’t just about cryptography—it’s about hardware, operating systems, and physical limits. Understanding how these layers interact deepened my perspective on efficiency, scaling, and design in distributed systems.
Ethical / Responsible Framing
This experiment was conducted entirely for educational purposes. It was not intended for production mining, profit generation, or participation in any live network at meaningful scale. The goal was learning—understanding concepts, not exploiting them.
I’m aware of the environmental and ethical discussions surrounding cryptocurrency mining, and I approached this exploration with that consciousness in mind. Running it locally for a limited period allowed me to learn responsibly without significant energy impact.
Keeping a clear separation between technical curiosity and commercial intent felt essential to maintaining a responsible developer mindset.
How This Fits My Broader Learning Path
This small experiment fits neatly into my broader learning journey across systems, backend development, and computer architecture. Exploring proof‑of‑work gave me a deeper sense of how low‑level mechanics—threading, performance, and resource management—connect to large‑scale system behavior.
It also complements my ongoing interest in Java, OOP design, and backend systems thinking, helping me see how fundamental computing tradeoffs shape higher‑level design decisions. Most importantly, it aligns with my “learning by building” philosophy: understanding complex ideas not just by reading about them, but by getting my hands dirty with real systems.
This experience will inform how I think about performance, scalability, and ethical design in future projects—connecting experimentation to long‑term, thoughtful practice.
Conclusion
This short experiment turned abstract talks about proof‑of‑work into something tangible and measurable. Running a sustained CPU‑bound workload on a regular laptop made it easy to see how factors like heat, power limits, and hardware design shape real‑world performance over time.
More than anything, it reminded me how valuable hands‑on experimentation can be for learning about systems. Even a small, time‑boxed test can uncover real tradeoffs that don’t stand out when everything stays theoretical.
Overall, this experiment gave me valuable exposure to how proof‑of‑work behaves at a system level and meaningful experience in observing real‑world performance tradeoffs.