LAMP-LLM: Look-Ahead Mixed-Precision Optimization for Large Language Model Inference

LAMP-LLM proposes the Look-Ahead Mixed-Precision inference optimization technique, addressing the bottleneck of Large Language Model (LLM) inference costs. By intelligently selecting precision strategies for different layers, it resolves the limitation of traditional "one-size-fits-all" quantization, significantly reducing computational overhead while ensuring generation quality, thus providing an efficient optimization solution for large-scale LLM applications.

LLM inference costs rise exponentially with parameter scale. Quantization is a mainstream optimization solution, but traditional global uniform precision strategies (e.g., global INT8/INT4) struggle to balance efficiency and quality, and manual layer-wise adjustment relies on expert experience which is hard to scale. Different layers have significant differences in precision sensitivity: attention layers (e.g., Query/Key computation) are sensitive, while FFN layers have strong fault tolerance.

Core Idea: Dynamically evaluate the sensitivity of subsequent layers via a look-ahead mechanism to make optimal precision choices. Key Steps: 1. Offline layer sensitivity analysis (construct sensitivity map); 2. Dynamic precision decision (select precision based on sensitivity within the look-ahead window); 3. Mixed-precision execution (high precision for sensitive layers, low precision for non-sensitive layers). Implementation Details: Supports per-tensor/per-channel/group-wise quantization; the look-ahead window can be adaptively adjusted; compatible with frameworks like vLLM and TensorRT-LLM, with custom CUDA kernel optimizations.

Experimental Setup: Tested models include Llama-2, Mistral, Qwen, etc.; evaluation tasks cover language modeling, question answering, and code generation; comparison baselines include FP16, global INT8/INT4, GPTQ, etc. Results: Efficiency improved by 2.5-3.5x, memory usage reduced by 60-75%; quality remains good (perplexity increase <5%, downstream task loss <2%); outperforms existing solutions like GPTQ and AWQ with a computational overhead increase <5%.

• High-throughput online services: Memory savings support more instances, combined with vLLM to maximize throughput;
• Edge devices: Can run on consumer GPUs/CPUs, combined with pruning and distillation techniques;
• Long-text inference: KV Cache quantization effectively improves sequence length processing capability.

Limitations: Relies on offline calibration data, requiring adjustments for different tasks; mainly optimized for NVIDIA GPUs; insufficient adaptation to advanced architectures like MoE and multimodality. Future: Explore online adaptive adjustment; improve optimization for platforms like AMD/Intel; support TPU/NPU and new model architectures.

LAMP represents the shift of LLM inference optimization from global uniform strategies to refined adaptive directions. It balances efficiency and quality through a look-ahead mechanism, providing practical optimization solutions for enterprises and developers. As model scales grow, such efficient inference technologies will become key infrastructure for LLM deployment.