Comparison guide
SuperCompress vs LLMLingua
LLMLingua uses a smaller LLM (Llama 2-7B) to compress prompts. SuperCompress uses a tiny 5K-parameter policy that runs on CPU with ~60ms latency.
Architecture differences
| Factor | LLMLingua | SuperCompress |
|---|---|---|
| Model size | 7B parameters | ~5K parameters |
| Hardware | GPU recommended | Runs on CPU |
| Latency | 500ms+ on GPU | ~60ms on CPU |
| Integration | Requires model download | pip install |
| Oracle recall | ~95% | 100% |
When to use each
LLMLingua works well when you have GPU access and need aggressive compression. SuperCompress is better for CPU-only deployments, serverless functions, and real-time applications where latency matters.
Frequently asked questions
Does SuperCompress need a GPU?
No. It runs on CPU with ~60ms latency.
Can I use both in my pipeline?
Yes. They are complementary approaches.
Build with less context
Put compression in front of your next LLM call.
Use the hosted API or run SuperCompress locally. Keep the evidence, drop the token waste, and measure the savings before it reaches your model.