Release notes.
For a physical AI lab.
Hardware changes, model changes, networking changes, failures, benchmark deltas, and conclusions—tracked like software releases instead of buried in blog posts.
LAB 0.4.1 changed + second 24 GB GPU + tensor split + airflow revision measured first-token latency tokens / second power draw thermals verdict keep the change
Experiments, not announcements.
Every entry should make the lab more reproducible. If the hardware, runtime, model, or benchmark method changes, the release notes should say exactly what changed.
Architecture defined
The project was organized around models, memory, compute, networking, agents, and security.
24 GB baseline
Planned baseline for inexpensive datacenter GPU inference, cooling, power, and model fit.
100GbE test
Find the first workload where faster Ethernet produces a measurable AI-lab advantage.
One experiment.
Documented the same way every time.
This is the reusable release-note format. The point is not decoration. It forces each test to state the hypothesis, configuration, method, result, verdict, and next step.
# hypothesis A second 24 GB GPU makes 70B Q4 inference practical without heavy CPU offload. # configuration 2 × 24 GB GPU 128 GB system RAM 70B Q4 model same prompt + context # method 1. record baseline 2. add second GPU 3. hold workload constant 4. measure latency + tok/s 5. record power # result model fit PASS throughput +31% power +46% noise regrettable # verdict Useful if acquisition cost is low. # next compare with one newer GPU
What every release should record.
A result without configuration is almost useless. These are the pieces that make the lab notes worth returning to later.
Hardware state
Server, CPU, RAM, GPU, PCIe placement, storage, network, cooling, and power.
Software state
OS, driver, runtime, model, quantization, context, flags, and relevant versions.
Measured result
Load time, first-token latency, tokens/sec, utilization, watts, thermals, and failures.
How the lab evolves.
Small versioned changes make it obvious which variable moved and whether the change survived testing.
Tensor split revised
Changed model distribution across two GPUs and re-ran the same prompt and context.
Cooling redesign
Added directed airflow and measured sustained throughput, thermals, and noise again.
Second compute node + 100GbE
Introduced a second host and tested when the network becomes part of the AI compute path.
“If the configuration changed, the benchmark changed.”
The release notes are the technical memory of the lab: what changed, what worked, what failed, and what should be tested next.