Efficient MLLMs: Innovations & Applications

Updated 29 November 2025

Efficient Multimodal LLMs are models that integrate text, image, and other modalities with specialized architectures and techniques to reduce computational and memory demands.
They leverage innovations like state-space models, low-rank adaptations, and adapter-based fine-tuning to enable rapid domain adaptation and edge deployment.
Advanced methods such as structured pruning, context sparsification, and aggressive quantization achieve up to 75% FLOP reduction with minimal accuracy loss.

Efficient Multimodal LLMs (MLLMs) are a class of models that integrate LLMs with capability for processing and understanding multiple input modalities—principally images, but expanding to video, audio, document layouts, and multi-turn interaction—while employing architectural and algorithmic strategies to systematically reduce computational, memory, data, and deployment costs relative to conventional MLLMs. Efficiency is achieved through innovations in backbone architectures (including state-space models and compression-aware transformers), fine-tuning protocols, system-level serving, hardware adaptation, and parameter reduction via pruning, low-rank adaptation, and context sparsification. These advances enable effective deployment on resource-constrained devices, rapid adaptation to novel domains (e.g., medical imaging), and scalable cloud inference for bursty, heterogeneous workloads.

1. Taxonomy and Core Principles

Efficient MLLMs can be categorized by their primary methods for reducing resource consumption (Jin et al., 17 May 2024):

Compact Architectures: Utilization of lightweight language backbones (e.g., Phi-2 2.7B, Gemma-2B, MobileLLaMA) and vision encoders (e.g., SigLIP, AIMv2, ViTamin).
Adapter/LoRA/Prompt-Based Fine-Tuning: Insertion of small, trainable bottleneck modules (adapters), low-rank decompositions (LoRA), or soft prompts into otherwise frozen (pretrained) MLLMs, confining updates to ≪3% of total parameters (Zhou et al., 7 Jun 2024, Zhao et al., 2023, Jin et al., 13 Oct 2025, He et al., 5 Jan 2024, He et al., 31 Oct 2024).
State Space Models (SSMs) and Non-Transformer Backbones: Replacing quadratic complexity self-attention backbones with linear-time state-space models (e.g., Mamba-2), yielding O(L) scaling for sequence length and constant memory per step (Huang et al., 29 Jul 2024).
Context and Token Sparsification: Actively pruning non-essential context tokens from the vision and/or language sequence during both prefill and autoregressive decoding, steeply reducing compute and memory with minimal accuracy loss (Huang et al., 1 Dec 2024).
Structural Pruning and Knowledge Distillation: Structured removal of redundant layers or hidden dimensions in the LLM followed by supervised and distillation-based recovery to restore (or closely recover) original accuracy at sharply lower inference cost (Huang et al., 28 Jul 2025, Wang et al., 2023).
Multistage/Hybrid System Serving: Elastic, phase-disaggregated online serving systems that dynamically allocate compute resources to preprocessing, encoding, prefill, and decoding to minimize latency and maximize throughput under mixed, real-world multimodal workloads (Liu et al., 14 Jul 2025, Liu et al., 16 Oct 2025).
Aggressive Quantization and On-device Techniques: Weight/activation quantization down to 2–4 bits, combined with LoRA and mobile-optimized kernel/pathways, supports real-time, low-power inference for edge deployment (Jin et al., 13 Oct 2025).

Crucial trade-offs in efficiency involve reductions in parameter count (often to <20% of baseline), FLOP reductions of 30–75%, memory footprint halving or better, and prefill/TTFT (time-to-first-token) acceleration by factors of 2×–10×, with typical accuracy drops remaining within 1–3%—and often negligible—on benchmark tasks (Jin et al., 17 May 2024, Jin et al., 13 Oct 2025, Yu et al., 16 Sep 2025).

2. Parameter-Efficient Fine-Tuning and Modular Adaptation

Parameter-efficient fine-tuning (PEFT) is central to efficient MLLMs. Adapters, LoRA, and LayerNorm-only tuning enable rapid adaptation of large frozen multimodal backbones to diverse downstream tasks using orders of magnitude fewer gradient updates and much lower GPU memory (Zhou et al., 7 Jun 2024, Zhao et al., 2023, He et al., 31 Oct 2024, He et al., 5 Jan 2024, Jin et al., 13 Oct 2025). The key approaches include:

Adapter Modules: Two-layer bottleneck modules inserted post-attention or after feed-forward sublayers, introducing only 2–3% additional parameters for full LLM scale (7B–13B) (Zhou et al., 7 Jun 2024).
LoRA (Low-Rank Adaptation): Insertion of trainable, low-rank delta matrices into query/key/value and output projections in each transformer block, keeping original weights frozen. LoRA parameter cost is directly proportional to rank r and layer dimensions, often yielding <1%–4% trainable parameter budgets (He et al., 31 Oct 2024, Zhou et al., 7 Jun 2024).
LayerNorm-Only Tuning: Tuning the scale and shift parameters of LayerNorm in each attention block yields >90% of the full-fine-tune performance while reducing trainable variables by >98% and GPU memory by >17% at 13B scale; this method is especially effective for domain adaptation (Zhao et al., 2023).
Prefix-Tuning, BitFit, and IA3: These offer even more extreme parameter reduction (<0.1% trainable), though often with reduced stability and generalization compared to adapters and LoRA (Zhou et al., 7 Jun 2024).

Empirical assessments indicate that adapters tune most robustly against overfitting and hallucination, with LoRA providing the strongest overall task accuracy at slightly higher parameter cost (Zhou et al., 7 Jun 2024). Tuning connector modules—the small MLPs that project visual features into the LLM space—is crucial for unseen tasks, while freezing them avoids overfitting for in-domain adaptation. All PEFT methods deliver up to 60–70% peak GPU memory savings and drastic reductions in wall-clock training time (He et al., 31 Oct 2024, He et al., 5 Jan 2024).

In the medical imaging domain, and in highly structured output regimes such as medical visual grounding, the combination of LoRA adapters and minimal new parameters outperforms both conventional MLLMs and specialized vision–language pretraining baselines (e.g., GPT-4v, MedKLIP, MSLL) with as little as 0.7–0.8% of parameters updated (He et al., 31 Oct 2024, He et al., 5 Jan 2024).

3. Efficient Backbone and Multimodal Connector Design

Efficient MLLMs systematically redesign backbone and connector modules to minimize compute and memory:

State Space Model (SSM) Backbones: ML-Mamba replaces the transformer's quadratic-time self-attention blocks with Mamba-2 SSMs, which compute causal convolutions in O(L) time; this enables processing of longer visual and language sequences without quadratic cost (Huang et al., 29 Jul 2024). The Mamba-2 Scan Connector (MSC) adapts 2D image tokens using scan and cross-scan mechanisms, efficiently fusing spatial and temporal information for downstream multimodal reasoning.
Composite Attention Mechanisms: EE-MLLM forgoes intra-visual token self-attention by using a composite attention operator: text-token queries, but keys/values from concatenated visual and text projections, with layers reusing existing LLM weights for vision–language alignment. This achieves compute savings of 30–70%, data efficiency (high accuracy at <1% of typical pretraining data), and state-of-the-art or better results versus LLaVA/Flamingo with much faster inference (Ma et al., 21 Aug 2024).
Unified Resampling Modules: MiniCPM-V 4.5 introduces a 3D-Resampler that groups and compresses both images and video into compact sequences, achieving up to 96× token reduction before projection into the LLM decoder. The result is highly scalable multimodal inference (e.g., for long documents, high-fps video) at a parameter overhead of <0.1% (Yu et al., 16 Sep 2025).
Context Sparsification: Dynamic-LLaVA employs lightweight, learned binary predictors to dynamically filter visual and output tokens deemed non-essential, enforcing sparsity in MHA and FFN operations both at prefill and autoregressive decoding stages. Measured savings include 75% less prefill FLOPs, ~50% lower decode compute and memory, and negligible to positive changes in VQA/generation accuracy (Huang et al., 1 Dec 2024).

4. Compression, Pruning, Quantization, and Edge Deployment

Compression techniques directly target the language backbone of MLLMs for efficient deployment:

Structured Pruning: Layerwise (block influence-based removal) and widthwise (importance-based neuron/head removal) pruning shrink the backbone to a user-specified fraction of parameters. For compression levels r ≲ 20%, widthwise pruning with projector-only recovery finetuning recovers ≥95% of baseline performance at 13–15% memory/FLOP reduction. Aggressive compressions (up to 60%) combined with knowledge distillation and hidden-state matching recover 70–90% of accuracy (Huang et al., 28 Jul 2025).
Quantization: Bit-level reduction (INT4–INT8) is routinely stacked with LoRA and pruning, with post- and quantization-aware fine-tuning (QALFT) techniques maintaining within 3% of full-precision accuracy at 30–50% memory reduction. AndesVL-4B INT4 models run in ~1.5 W on mobile NPU, providing ≤ 150 ms/token latency for GUI and VQA applications (Jin et al., 13 Oct 2025).
On-device Optimization and LoRA Adapters: 1+N LoRA schemes (base model plus lightweight adapters per application) allow multiple task-specific deployments without duplicating the full model. Pixel-shuffle and NaViT strategies reduce visual token counts at minimal accuracy cost (Jin et al., 13 Oct 2025).

5. Efficient Multimodal Serving Systems

Efficient serving frameworks are critical to exploiting efficient MLLMs at scale:

Phase-Disaggregated Serving: ElasticMM and xLLM decompose inference into Encode, Prefill, and Decode pools, adaptively allocating GPU resources for each phase and across request types (modality-aware), responding in real time to bursty and heterogenous demand (Liu et al., 14 Jul 2025, Liu et al., 16 Oct 2025).
Hybrid Cache and Speculative Execution: LRU-managed prefix caches for multimodal inputs and asynchronous encoding dramatically lower TTFT and enable linear scaling under concurrent workloads (Liu et al., 14 Jul 2025). Virtual address space (xTensor) and incremental global KV cache offloading allow extension to very long context and external memory (Liu et al., 16 Oct 2025).
Instance Elasticity and Fault Tolerance: Stateless elastic instance pools enable rapid reallocation and recovery in the event of faults or SLO violations, achieving <200 ms RTO for online workloads and 1.7–2.2× throughput over MindIE/vLLM-Ascend benchmarks (Liu et al., 16 Oct 2025).
Streaming/Infinite Inference: Inf-MLLM achieves efficient infinite-context inference on a single GPU by dynamically maintaining a fixed-size relevant KV cache, exploiting learned "attention saddle" patterns and linear attention biasing. This enables stable reasoning and video QA at >1-hour context length, 2× the throughput of competing methods (Ning et al., 11 Sep 2024).

6. Domain Adaptation, Self-improvement, and Specialized Efficient MLLMs

Specialized adaptations and self-improvement mechanisms further expand the applicability and efficiency envelope of MLLMs:

Medical Visual Grounding and Reporting: PFMVG and PeFoMed demonstrate that fine-tuning only 0.7–0.8% of MLLM parameters is sufficient to reach SOTA performance in structure-constrained medical tasks, surpassing GPT-4v and vision-language pretraining baselines (He et al., 31 Oct 2024, He et al., 5 Jan 2024).
Judge-free, CLIP-based Self-Improvement: Efficient self-improvement in MLLMs is achieved by controllable hallucination sampling and CLIP-based preference evaluation, removing the large compute requirement of in-model RL or self-judgment. Preference learning via direct optimization yields notable gains in hallucination control and F1 with orders-of-magnitude less compute (Deng et al., 26 Nov 2024).
Continual Learning and Cloud-Device Collaboration: Cloud-device frameworks leverage uncertainty-guided token sampling, adapter-based knowledge distillation, and dynamic downlink compression (DWC) to keep a small device-side MLLM updated with minimal uplink/downlink cost (up to 99.9% reduction in transmitted bytes), validated in real-robot deployments (Wang et al., 2023).
Video, Document, and Multi-modal Long-context: Temporal Grounding Bridge (TGB) bootstraps long-context video understanding in MLLMs, using learnable optical-flow projections, RoPE-based extrapolation, and Gumbel-Softmax multi-span reading comprehension heads; this enables efficient and accurate video QA far beyond original window limits at only ~7% computation overhead (Wang et al., 25 Feb 2024).

7. Limitations and Future Directions

Despite considerable advances, several challenges persist (Jin et al., 17 May 2024, Liu et al., 16 Oct 2025, Huang et al., 1 Dec 2024, Huang et al., 29 Jul 2024):

Multi-image/Video Scalability: Most efficient MLLM architectures are single-image/multi-turn; scaling to high-fps video, document chains, or complex layouts remains difficult without further backbone changes (e.g., spatio-temporal SSMs).
Rare Modality Support: Integration of audio, depth, and 3D remains rare; future models will require unified or adaptive connectors for broad sensor coverage.
Connector and Adapter Complexity: As vision–language alignment becomes more challenging, the projector/fusion layer (Q-Former, MLP) can dominate new-parameter budgets.
Edge-specific Quantization and Compilation: Further progress in 1–2 bit quantization, compiler-level kernel optimization (e.g., FlashAttention 2.0), and head-pruning is required for sub-1B model deployment on microcontrollers and NPUs.
Unified Long-context Models: Directions include token-gated MoEs, dynamic per-layer context pruning (Huang et al., 1 Dec 2024), and revisiting architectures such as Mamba-2 and hybrid SSM networks for sequence scaling (Huang et al., 29 Jul 2024).
Reward Modeling and Data Efficiency: Interactive RL and human-feedback (RLAIF) pipelines must scale without overwhelming compute budgets; open, structured multimodal datasets (Multimodal C4) remain a key bottleneck.

Efficient MLLMs thus continue along multiple axes of optimization—algorithmic, architectural, and systems-level—towards universal and accessible multimodal inference.