{
 "schema_version": 1,
 "generated": "2026-10-26",
 "id": "lyogavin-airllm--speed-up-airllm-with-compression",
 "repo": "lyogavin/airllm",
 "repo_url": "https://github.com/lyogavin/airllm",
 "repo_slug": "lyogavin-airllm",
 "title": "Speed up large-model inference with 4-bit block compression",
 "page_title": "How to speed up large-model inference with 4-bit block compression with Airllm",
 "summary": "Load a model with compression set to 4bit or 8bit for faster runs on the same small GPU.",
 "who": "A researcher whose AirLLM runs work but are too slow for iteration.",
 "problem": "Streaming full-precision layers from disk is the bottleneck on small GPUs.",
 "outcome": "Faster inference with block-wise 4-bit or 8-bit compression on the same hardware.",
 "steps": [
  "Install AirLLM and update bitsandbytes with `pip install -U bitsandbytes`",
  "Load your model with compression='4bit' (or '8bit') as in the Model Compression example",
  "Run the same prompt with and without compression",
  "Compare speed and output quality",
  "Keep the setting that gives acceptable quality"
 ],
 "agent_prompt": "Using lyogavin/airllm, load <model> twice, once normally and once with compression='4bit', run the same three prompts on both, and give me a table of time per answer and any quality differences.",
 "agents": [
  "claude-code",
  "codex"
 ],
 "difficulty": "Intermediate",
 "time": "1 hour",
 "basis": "readme",
 "caveat": "",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "jupyter notebook",
 "requirements": [
  "Python and pip",
  "A CUDA GPU (a few GB of VRAM) or a Mac with mlx and torch for Llama-style models",
  "Lots of disk for split layer shards; some models need specific transformers versions",
  "Optional: bitsandbytes for compression"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/lyogavin-airllm/speed-up-airllm-with-compression/",
 "md": "https://dedupedai.com/use-cases/lyogavin-airllm/speed-up-airllm-with-compression/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/lyogavin-airllm--speed-up-airllm-with-compression.json",
 "api_version": "v1"
}