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How to speed up large-model inference with 4-bit block compression with Airllm

Load a model with compression set to 4bit or 8bit for faster runs on the same small GPU.

Short answer: Install AirLLM and update bitsandbytes with pip install -U bitsandbytes; then load your model with compression='4bit' (or '8bit') as in the Model Compression example; run the same prompt with and without compression. All steps.

Who it is forA researcher whose AirLLM runs work but are too slow for iteration.

The problemStreaming full-precision layers from disk is the bottleneck on small GPUs.

The outcomeFaster inference with block-wise 4-bit or 8-bit compression on the same hardware.

What you need first

  • 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

Steps

  1. Install AirLLM and update bitsandbytes with pip install -U bitsandbytes
  2. Load your model with compression='4bit' (or '8bit') as in the Model Compression example
  3. Run the same prompt with and without compression
  4. Compare speed and output quality
  5. Keep the setting that gives acceptable quality

Prompt for your agent

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.

Caveat.

Use this in your AI agent

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Using the Deduped MCP, get the setup steps for lyogavin/airllm and "Speed up large-model inference with 4-bit block compression"

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