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Compute Testing Framework

I built compute because I wanted to see what really happens when code runs on modern compute systems.
Not just numbers or theory. I wanted to understand what works, what matters, and what is worth using in real developer and IT workflows.

Most existing benchmarks focus too much on ideal conditions, academic setups, or marketing slides.


On this blog compute.kim, I take a practical, developer-first approach. Every test and experiment on this site follows a simple, transparent framework:

Framework Overview

1. What are we testing?

Clearly define the compute system and the target workload.
Example: GPU training of LLaMA 3 7B, multi-node cluster inference of Stable Diffusion, cloud TPU training run, etc.

2. Performance

Measure real-world performance.
How fast does it run? What is the throughput? What is the latency?
Benchmarks are run with real datasets, on real hardware.

3. Scalability

How well does the system scale?
Single device → multi-device → multi-cluster.
Are there diminishing returns? What are the scaling bottlenecks?

4. Cost-efficiency

Is it worth the cost?
I measure cost per training step, per inference, or per job...using actual cloud or hardware rental costs.

5. Reproducibility

Can others reproduce this?
I provide code, configuration details, and datasets if possible.
Results should be verifiable, not "magic numbers".

6. Security considerations

Are there relevant security concerns?
If applicable, I highlight potential risks such as data leakage, shared tenancy issues, or hardware isolation concerns.

7. Reliability

Does the system perform consistently?
Is performance stable over time and under different loads?
Do we see crashes, failures, or instability in longer runs?

8. Practical usability

Is this compute setup usable for real developers and IT teams?
Does it require exotic knowledge or special tuning?
How practical is it to adopt in normal engineering workflows?

9.Typical problems and issues

What problems or common pitfalls did I encounter?
Are there known issues with this stack?
What should devs or IT folks watch out for when using this setup?

Why this framework?

This framework is inspired by real-world needs from:

  • Site Reliability Engineering (SRE) principles
  • MLPerf benchmarking practices
  • CNCF and cloud-native computing patterns
  • AWS Well-Architected Framework

But it is simplified and adapted for the developer, IT, and DevOps community.
It is designed to answer the key questions developers, infra engineers, and teams actually care about:

  • Will this compute stack work for me?
  • Is it worth the cost?
  • Can I reproduce these results?
  • Will it scale reliably?
  • Is it practical to use?

Summary

"Every test on compute.kim follows this framework: What are we testing? Performance. Scalability. Cost-efficiency. Reproducibility. Security. Reliability. Practical usability."

I aim to make this process clear and transparent in every post...so you know exactly what was tested, how it was tested, and why the results matter.