A practical guide to deploying Large Language Models Cheap, Good *and* Fast. Joel Kang’s extremely comprehensive notes on what he learned trying to run Vicuna-13B-v1.5 on an affordable cloud GPU server (a T4 at $0.615/hour). The space is in so much flux right now—Joel ended up using MLC but the best option could change any minute.
Vicuna 13B quantized to 4-bit integers needed 7.5GB of the T4’s 16GB of VRAM, and returned tokens at 20/second.
An open challenge running MLC right now is around batching and concurrency: “I did try making 3 concurrent requests to the endpoint, and while they all stream tokens back and the server doesn’t OOM, the output of all 3 streams seem to actually belong to a single prompt.”
Recent articles
- How StrongDM's AI team build serious software without even looking at the code - 7th February 2026
- Running Pydantic's Monty Rust sandboxed Python subset in WebAssembly - 6th February 2026
- Distributing Go binaries like sqlite-scanner through PyPI using go-to-wheel - 4th February 2026