Definitive Guides
Evergreen pillar pages that explain the technical landscape and create the site’s core authority.
Static-first authority site
TurboQuant, LLM quantization, and efficient inference explained with engineering discipline.
Clear guides for engineers working on TurboQuant, 4-bit inference, and production LLM efficiency. The first production pillar page is now live, and the rest of the authority-site architecture is already in place for fast follow-on publishing.
5
pillar clusters mapped from keyword research
20+
supporting article slots planned for launch
95+
target PageSpeed score with static export
Next priority pages
One flagship guide is live, and the next routes are already mapped.
These are the highest-leverage pages queued after the published TurboQuant pillar. The site structure is already wired so each one can ship without another architecture pass.
Next to publish
The Complete Guide to LLM Quantization
Foundational taxonomy covering PTQ, QAT, weight quantization, activation quantization, and KV-cache compression.
Next to publish
TurboQuant vs GPTQ
Decision-stage comparison clarifying when KV-cache compression matters more than weight-only quantization.
Next to publish
Reduce LLM VRAM and Inference Cost
Operational playbook for lowering memory footprint, latency, and serving cost without blind tuning.
Next to publish
How to Implement TurboQuant in Python
Runnable setup guide for the first practical integration path, quality checks, and memory validation.
Content system
Three route families, each tuned to a distinct search intent.
Comparison Pages
Decision-stage pages for engineers comparing quantization methods, benchmark tradeoffs, and deployment fit.
Implementation Tutorials
Runnable implementation pages focused on practical setup, memory budgets, and hardware-constrained inference.
Publishing queue
The first article is published; the next wave stays visible.
The live TurboQuant guide now sits alongside the remaining queued routes, each with a keyword target and page intent ready for the next content sprint.
Outline ready18 min
The Complete Guide to LLM Quantization
A pillar page covering the full quantization landscape, from PTQ and QAT to KV-cache compression and model format choices.
LLM quantizationUpdated 2026-04-22
Published16 min
TurboQuant Explained: How It Works, Benchmarks & Tutorial
TurboQuant is Google's breakthrough KV cache quantization. Learn how it works, how it compares to GPTQ/AWQ/GGUF, see benchmarks, and follow a step-by-step implementation tutorial.
turboquantUpdated 2026-04-22
Research queued12 min
Reduce LLM VRAM and Inference Cost
A practical guide to shrinking memory pressure and improving serving efficiency with quantization and cache-aware techniques.
reduce LLM VRAM usageUpdated 2026-04-22
Benchmark table placeholder10 min
TurboQuant vs GPTQ
A comparison page clarifying where TurboQuant complements GPTQ and where each method fits in the inference stack.
turboquant vs GPTQUpdated 2026-04-22
Outline ready11 min
GPTQ vs AWQ vs GGUF
A core decision page comparing the three formats most often considered for local and production inference workloads.
GPTQ vs AWQ vs GGUFUpdated 2026-04-22
Code sample slot reserved9 min
How to Implement TurboQuant in Python
A step-by-step placeholder for the practical implementation guide covering environment setup, code structure, and validation workflow.
implement turboquant pythonUpdated 2026-04-22