LiteRT-Optimized INT8 LLM for Raspberry Pi4 Deployment
Bibliographic record
Abstract
Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks. However, their high computational and memory requirements pose significant challenges for deployment on resource-constrained edge devices such as the Raspberry Pi. In this work, we investigate post-training quantization techniques to reduce the computational burden of LLMs while preserving their quality. We evaluate several LLMs under different precision settings and show that 8-bit quantization, especially when combined with runtime-level optimizations like LiteRT achieves up to 2× faster inference on Raspberry Pi, compared to framework-native formats, without relying on hardware-specific acceleration libraries (e.g., GPU, NNAPI, or EdgeTPU), and with negligible degradation in output quality. Our experiments highlight the practicality of lightweight LLM deployment on edge devices. These findings demonstrate the feasibility of real-time applications on low-power devices, enabling broader accessibility in edge environments. Our project page is available at https://rlghksdbs.github.io/EfficientLLM/.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".