LiteRT-Optimized INT8 LLM for Raspberry Pi4 Deployment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it