DGX Spark Handbook (EXO Labs)
- Raw:
raw/articles/2026-exolabs-dgx-spark-handbook.md - Original: https://x.com/exolabs/status/2103617535765573959
- Author: @0xSero, published by EXO Labs, 2026
A practical guide to running local LLMs on one to four DGX Sparks. It’s the model for the article Nima wants to write about beast.
Big ideas, one line each
These are filled in gradually as each one becomes a lesson.
- Memory bandwidth decides how fast a model writes tokens. ✅ covered
- Reading the prompt (prefill) and writing the answer (decode) are two different jobs with different limits.
- Quantisation shrinks a model so it fits, at some cost to quality.
- Mixture-of-experts models, speculative-decoding and batching are the three main ways to go faster.
- Local can feel as fast as the cloud, because the cloud splits its GPUs across many users.
Worth copying for Nima’s own article
- Every speed number names the task and prompt length it came from.
- It’s structured as a step-by-step path, and the reader can stop at any step.
- Deeper “why” material is kept in advanced sections at the end.