#quantization
6 posts tagged with #quantization
Every article below is hand-written, technically reviewed, and focused on quantization. Posts cover real-world architecture decisions, code-level implementation patterns, and trade-offs you'll only discover after shipping production systems.
Developer Tools LM Studio Power User Setup 2026: Profiles, Quants, LAN Serving
My opinionated 2026 LM Studio setup: reproducible model profiles, quant hygiene rules, scripted loads via lms + /api/v1, and a secure multi-user LAN server.
AI and Machine Learning How to Run Qwen 35B on 16GB VRAM [2026]: Flags + Quants
A reproducible 16GB recipe for “Qwen 35B”: which Qwen2.5-32B quants fit, how to budget KV cache, and the exact serving flags that stop OOMs.
Technology Local LLM Benchmark Methodology [2026]: TTFT vs tok/s Done Right
Stop screenshot-benchmarking. Here’s a reproducible local LLM benchmark methodology for 2026 that separates TTFT from throughput and reports rerunnable results.
AI and Machine Learning Gemma 4 26B CPU Inference Benchmark: 5 tok/s Production Math [2026]
A $300 Xeon from 2013 runs Gemma 4 26B at 5 tok/s with no GPU. Here's the memory bandwidth math, quantization tradeoffs, and production decision framework nobody else is covering.
AI and Machine Learning LLM Quantization Levels Compared: Q4_K_M vs Q8_0 vs FP16 [2026]
The practitioner's guide to choosing between Q4_K_M, Q5_K_S, Q8_0, and FP16 quantization for local LLMs — with real perplexity numbers, throughput benchmarks, and per-use-case recommendations.
![GGUF vs GPTQ vs EXL2: LLM Quantization Compared [2026]](https://img.kunalganglani.com/images/vzekdneq/production/34e429bea58e21d5119b5baad6b9efe44974be77-1200x675.webp?auto=format&fit=max&q=75&w=600)
AI and Machine Learning GGUF vs GPTQ vs EXL2: LLM Quantization Compared [2026]
A head-to-head comparison of GGUF, GPTQ, and EXL2 quantization formats with real quality, speed, and VRAM trade-offs — updated for the 2026 Hugging Face acquisition of ggml.ai.