What does provenance-aware priority handling do?

It prevents untrusted RAG, tool, and assistant content from gaining protected priority solely through imperative or safety-critical wording.

Does LLMSlim prevent prompt injection?

No. It mitigates compression-induced instruction elevation and applications still need defense in depth.

Does LLMSlim require remote calls?

The default extractive strategy runs locally. Rewrite and hybrid strategies use a caller-supplied provider.

All engineering notes

Benchmarking Methodology 9 min read Yashvardhan Thanvi July 15, 2026

Architectural Comparison: Offline Graph Methods vs. Neural Perplexity Pruning

Evaluating Throughput, Hardware Footprint, and Latency Metrics for Edge & Cloud Gateways

Mathematical intuition

Neural methods calculate conditional perplexity H(x_i | x_<i) using a small local model; offline graph methods compute TF-IDF cosine centrality over CPU matrices.

Key takeaways

01Offline graph compression incurs zero GPU memory allocations and minimal CPU overhead.
02Neural compression uses small LMs to evaluate token perplexity but introduces model cold-start and VRAM dependencies.
03Hybrid architectures combine rule-based priority locking with CPU graph centrality for reliable API gateways.

1. Operational Tradeoff Matrix

System requirements and trade-offs between offline graph compressors and neural perplexity models:

Dimension	Offline Graph (LLMSlim)	Neural Perplexity (LLMLingua)
GPU VRAM Requirement	0 MB (Pure CPU)	2,048 MB - 8,192 MB
External Dependencies	None (Standard NumPy / SciPy)	PyTorch / Transformers Model Weights
Execution Characteristics	Deterministic P99 Latency	Varied by Batch Size & GPU Queue
Instruction Shielding	Explicit Rule-Based Locking	Probabilistic Perplexity Threshold
Deployment Targets	Lambda, Edge, CLI, Microservices	GPU Node Clusters

References

[Jiang et al. (2023)]LLMLingua: Compressing Prompts for Accelerated Inference (EMNLP 2023)