[Paper] Quantized-Tinyllava: a new multimodal foundation model enables efficient split learning

Source: arXiv - 2511.23402v1

Overview

The paper Quantized‑TinyLLaVA tackles a long‑standing bottleneck in split learning: the massive bandwidth required to shuttle high‑dimensional embeddings between client and server when using large multimodal foundation models. By integrating a learnable quantization layer that compresses embeddings into ultra‑low‑bit integers, the authors dramatically cut communication overhead while keeping model quality intact—making privacy‑preserving, distributed AI far more practical for real‑world deployments.

Key Contributions

• Learnable low‑bit quantization for multimodal embeddings that can be trained end‑to‑end with the backbone model.
• Theoretical grounding: Derivation of the optimal number of discrete representation levels using entropy‑coding principles, ensuring the compression is information‑theoretically efficient.
• Split‑learning‑ready architecture: A modular redesign of TinyLLaVA that cleanly separates client‑side feature extraction from server‑side language reasoning, with the quantizer sitting at the interface.
• Empirical validation: Demonstrates >10× reduction in transmitted data with <1 % drop in downstream vision‑language tasks (e.g., VQA, image captioning).
• Open‑source implementation and benchmark scripts for reproducing the results on common multimodal datasets.

Methodology

1. Model Partitioning – The multimodal foundation model (TinyLLaVA) is split into two parts:

   • Client side: visual encoder (e.g., ViT) that processes raw images and produces a high‑dimensional embedding.
   • Server side: language decoder (LLaVA) that consumes the embedding to generate text.

2. Learnable Quantizer – Before the embedding leaves the client, a small neural network learns to map the 32‑bit floating‑point vectors to k‑bit integers (k = 2–4 in experiments). The quantizer is trained jointly with the downstream task loss, so it learns to preserve the most task‑relevant information.

3. Entropy‑Based Level Selection – Using Shannon entropy, the authors compute the minimal number of quantization levels that can represent the embedding distribution without exceeding a target distortion. This yields a closed‑form rule for picking k based on the empirical variance of the embeddings.

4. De‑quantization on Server – The server reconstructs a floating‑point approximation from the low‑bit integers using a learned inverse mapping, then feeds it to the language decoder.

5. Training Pipeline – The entire pipeline (visual encoder → quantizer → de‑quantizer → language decoder) is trained end‑to‑end on standard multimodal benchmarks, with an additional regularization term that penalizes quantization error.

Results & Findings

• Communication savings: Even with a conservative 4‑bit setting, the data sent from client to server shrinks by an order of magnitude, directly translating into lower latency and cheaper network usage.
• Performance impact: The drop in downstream task scores stays under 1 % for 4‑bit and under 2 % for 2‑bit, which is often acceptable given the bandwidth gains.
• Scalability: Experiments on larger multimodal models (e.g., LLaVA‑13B) show similar compression‑vs‑accuracy trade‑offs, suggesting the approach generalizes beyond TinyLLaVA.

Practical Implications

• Edge‑to‑cloud AI: Devices like smartphones, AR glasses, or IoT cameras can run the visual front‑end locally, compress the embeddings, and stream them efficiently to a powerful cloud language model.
• Privacy‑first services: Since raw images never leave the device, compliance with GDPR, HIPAA, or other data‑protection regulations becomes easier, while still enabling rich multimodal interactions (e.g., on‑device visual assistants).
• Cost reduction: Enterprises deploying split‑learning pipelines can cut bandwidth bills dramatically, especially in scenarios with many concurrent users (e.g., large‑scale visual QA platforms).
• Plug‑and‑play quantizer: The quantization module is lightweight (a few thousand parameters) and can be inserted into existing split‑learning stacks with minimal code changes.

Limitations & Future Work

• Quantizer overhead: Although small, the additional forward pass for quantization/de‑quantization adds a few milliseconds of latency on low‑power devices.
• Task‑specific tuning: The optimal k varies across tasks; a one‑size‑fits‑all setting may not be ideal for highly sensitive downstream applications.
• Robustness to distribution shift: The entropy‑based level selection assumes a stationary embedding distribution; sudden changes (e.g., new visual domains) could degrade compression efficiency.

The authors propose extending the framework to adaptive quantization, where the client dynamically selects the bit‑width based on network conditions, and exploring hardware‑accelerated integer arithmetic to further shrink latency.

Quantized‑TinyLLaVA demonstrates that smart, learnable compression can unlock the practical deployment of large multimodal models in privacy‑sensitive, bandwidth‑constrained environments—an advance that should excite both AI researchers and engineers building the next generation of edge‑cloud AI services.

Authors

• Jiajun Guo
• Xin Luo
• Jie Liu

Paper Information

• arXiv ID: 2511.23402v1
• Categories: cs.LG, stat.ML
• Published: November 28, 2025
• PDF: Download PDF