FIELD NOTES FOR AI ENGINEERS

AI Infrastructure,
Tested in the
Real World.

Practical guides, benchmarks, and troubleshooting for LLM inference, GPUs, vLLM, SGLang and production AI infrastructure.

Reproducible by design. Transparent about the evidence.
● ● ● inference-lab / research notes
THE INFERENCE STACK
01 MODELQwen · DeepSeek · Llama
02 ENGINEvLLM · SGLang · llama.cpp
03 HARDWAREGPU · CUDA · Interconnect
Understand. Deploy. Measure._
01

Inference

Engines, serving & optimization

02

GPU & Hardware

Memory, drivers & compatibility

03

Troubleshooting

From error log to root cause

04

Benchmarks

Measurements with context

THE LAB NOTEBOOK

Engineering, beyond the demo.

Deployment guides and technical notes with their evidence status clearly marked.

INFERENCE · EDITOR'S PICK

vLLM vs SGLang:
Choosing an Inference Engine

Start with your workload, not a leaderboard. A practical framework for evaluating compatibility, latency and operational complexity.

Read the guide ↗
vLLMvsSGLangONE WORKLOAD. A FAIR COMPARISON.
FRESH FROM THE NOTEBOOK

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TROUBLESHOOTING

When the GPU disappears,
start with the evidence.

A diagnostic approach to NVIDIA Xid 79, with read-only commands and a clear recovery checklist.

Investigate Xid 79 ↗
BENCHMARK NOTEBOOK

No numbers without
the experiment.

Our first results are awaiting real-world testing. See the measurement standards behind every future report.

Explore benchmark methodology ↗

Tools for the next deployment

Memory estimators and serving configuration helpers are on the roadmap.

View planned tools →