Independent LLM Engineering Journal
Empirical LLM benchmarks and quantization analysis
128k
Context window evaluation
4-bit
Quantization loss curves
0%
Marketing fluff guaranteed
Core Pillars
Examining the underlying mechanics of open-weight models under production constraints.
Memory
Attention
Throughput
Quantization loss
Context window breakdowns
Inference latency
Evaluating perplexity drift across GPTQ, AWQ, and GGUF compression formats on standard benchmark suites.
Tracking retrieval degradation and attention dissipation across extended 128k sequence lengths.
Measuring time-to-first-token and sustained generation speeds under concurrent multi-tenant loads.
Editorial Standard
No hype, no vague futures—just raw token throughput and clean Python implementations.
Built for machine learning engineers who need reproducible metrics over press releases.
Explore the complete technical archive
Access all published architecture breakdowns, code repositories, and benchmark datasets.