A deep technical breakdown of 1-bit quantization in LLMs using the Bonsai-8B model. Exploring binary neural networks, inline dequantization kernels, and achieving 14x compression with minimal quality loss.
Situation Deploying LLMs on edge devices with severe memory constraints. Standard FP16 models require 16GB+ VRAM, pricing out most users from running capable models locally.
Used In Edge LLM deployment, RK3588 inference, high-throughput serving
local-aiquantizationbonsaibinary-neural-networks
Publishing a practical local-AI control plane for llama.cpp: remote model loading, runtime tuning, streaming chat, and real REAP model serving on a Radxa ROCK 5B+.
Situation I wanted a serious remote control surface for local GGUF inference on a Radxa ROCK 5B+ instead of one-off shell commands or generic UIs that hide the important llama.cpp knobs.
Used In Local-first AI serving on a Radxa ROCK 5B+ / RK3588 using source-built llama.cpp and GGUF models, including GLM-4.7-Flash-REAP-23B-A3B.
llama.cpprk3588radxarock-5b-plus
An advanced benchmark report for running Qwen3.5 locally on RK3588 with source-built llama.cpp: prefill speed, decode speed, stable context, tool-calling behavior, and the practical model choices that actually work on a Radxa ROCK 5B+.
Situation I was tuning a local-first Discord engineering agent on a Radxa ROCK 5B+ (RK3588, 24 GB RAM) and needed hard data for which Qwen3.5 models were actually practical on CPU inference with llama.cpp.
Used In Local-first Discord agent runtime on Radxa ROCK 5B+ / RK3588, built around raw llama.cpp rather than Ollama or LM Studio.
rk3588radxarock-5b-plusllama.cpp
An advanced guide to local GPU inference with llama.cpp: why bandwidth matters more than model fit, how hybrid GPU+CPU offload behaves on cards like the RTX 3060 and 5070, what quantization really means mathematically, and how to run it on Linux, Windows, and WSL.
Situation Teams running local models on consumer GPUs often assume that if a model loads, it is production-ready. In practice, once model layers or KV cache spill from VRAM into system RAM, the system hits a bandwidth cliff and throughput collapses.
Used In Local-first AI engineering runtimes and workstation inference setups using llama.cpp on consumer NVIDIA GPUs.
local-aillama.cppcudavram
How I implemented hybrid per-layer KV cache quantization on RK3588 using insights from Google's TurboQuant research, achieving 17% better compression with zero quality loss.
Situation Running a local-first Discord AI agent (Engram) on a $130 RK3588 single-board computer with 24GB RAM. The challenge: KV cache memory during long conversations would crash the bot or cause severe latency spikes.
Used In Engram AI Discord bot, RADXA AI Suite
local-aiedge-aiturboquantkv-cache
A deep dive into the cloud architectures, real-time data streaming capabilities, and Generative AI setups powering Formula 1 and Formula E in 2026.
Situation Top-tier motorsport series (F1 and FE) introduced radical new technical regulations in 2026, causing an explosion in telemetry data (over 1.1 million data points per second) that legacy systems couldn't process in real-time.
Used In Researching modern high-throughput IoT edge-to-cloud architectures for autonomous vehicle frameworks.
system-architecturedata-engineeringawsgoogle-cloud