User burns GPT-6 Astra weekly quota in three days, switches to local models
The author says they used up their Astra weekly quota in three days, so they run GLM-5.3 Flash, Kimi K3 and DeepSeek V4 Flash locally with the open-source colibri project and share the full setup steps.
i burned through my entire GPT-6 Astra weekly quota in 3 days so i'm replacing my $200/mo Codex subscription with this... a free 32,960-star GitHub repo that runs GLM-5.3 Flash, Kimi K3, and DeepSeek V4 Flash locally [here's the exact setup i used:] 1. get colibri download the release archive for Linux, macOS, or Windows. no compiler needed. python3 coli info 2. or build it from source git clone https://github.com/JustVugg/colibri cd colibri/c ./setup.sh cd .. 3. pick a model GLM-5.3 Flash: 321B, ~195 GB converted, 25 GB RAM DeepSeek V4 Flash: 284B, ~167 GB, or ~85 GB for the REAP 150B version Kimi K3: 2.8T, ~1.6 TB snapshot, 32 GB+ RAM none of them needs a GPU. a GPU only makes some runs faster. 4. get the weights GLM-5.3 Flash uses the dedicated converter: python3 tools/convert_glm53.py --outdir /nvme/glm53_i4 --min-free-gb 30 DeepSeek V4 Flash and Kimi K3 load from their original Hugging Face snapshots. pre-converted containers work too. 5. run it COLI_MODEL=/nvme/glm53_i4 ./coli chat ./coli web --model /nvme/glm53_i4 # dashboard ./coli serve --model /nvme/glm53_i4 # headless API 6. tune it ./coli doctor # readiness check ./coli plan # RAM/VRAM/disk placement ./coli tune # fastest safe profile `coli` reads `config.json` and picks the right engine for each model. the tradeoff is honest: no token bill, no hosted quota... but you need the storage. the frontier moved from your subscription to your SSD.
This post quotes another post: x.com/av1dlive/status/2098055179761525165