Token-Operations-Oriented Inference Optimization Techniques for Large Models

2026-02-24 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Expert, extended

Summary

Token-Operations-Oriented Inference Optimization Techniques for Large Models introduces a four-layer technical architecture to enhance the scalable, low-cost, and stable operation of large model services, crucial given China's average daily token processing volume exceeded 140 trillion by March 2026. This framework comprises "Multi-model Fusion," "Model Optimization," "Compute-Model Fusion," and "Compute-Network-Model Fusion." It systematically reviews key technologies across these levels, aiming to reduce token production costs, improve service efficiency, and ensure stable token supply. The approach facilitates the evolution of large model services from basic API accessibility to sustainable operational capability, addressing challenges like massive requests, high-concurrency, and multi-model collaboration.

Key takeaway

For AI Architects and Machine Learning Engineers deploying large language models, prioritize a holistic, four-layer inference optimization strategy. Implement intelligent routing and model cascading for cost-effective request handling, and leverage KV cache compression and dynamic batching to maximize hardware utilization. Focus on balancing quality, cost, latency, and throughput across your entire service pipeline to ensure scalable and stable token operations.

Key insights

Token-oriented inference optimization requires a four-layer architecture to balance quality, cost, and stability for large model services.

Principles

• System-level coordination is vital for large model inference optimization.
• Balance quality, cost, latency, throughput, stability, and security.
• Token volume growth demands large-scale token operations.

Method

A four-layer architecture: Multi-model Fusion, Model Optimization, Compute-Model Fusion, and Compute-Network-Model Fusion, systematically reviews and applies key technologies.

In practice

• Implement intelligent routing for cost-effective request dispatching.
• Employ speculative decoding to accelerate token generation.
• Optimize KV cache and dynamic batching for resource efficiency.

Topics

• LLM Inference Optimization
• Multi-model Orchestration
• KV Cache Management
• Speculative Decoding
• Model Quantization
• MoE Architectures

Code references

• aurelio-labs/semantic-router
• zilliztech/GPTCache
• BerriAI/litellm
• mem0ai/mem0
• deepseek-ai/DeepEP

Best for: Research Scientist, MLOps Engineer, AI Engineer, AI Scientist, Machine Learning Engineer, AI Architect

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• KV Cache Demystified: Speeding Up Large Language Models — KV cache optimizes LLM inference by storing and reusing attention keys and values, preventing redundant computations.
• Token-Operations-Oriented Inference Optimization Techniques for Large Models — Token-operations-oriented inference optimization uses a four-layer architecture to make large model services operable.
• AI 101: From Tokens to Answers: What Actually Happens During LLM Inference — Modern LLM inference is a complex system orchestration problem balancing latency, cost, and resource efficiency across distinct prefill and decode phases.
• Sub-Token Routing in LoRA for Adaptation and Query-Aware KV Compression — Sub-token routing offers fine-grained control for transformer efficiency by compressing within token representations.
• Inference Optimization in LLMs: A Systems View — Inference optimization for LLMs is a layered systems problem, not a single technique, where memory bandwidth is the primary constraint.
• Hybrid Verified Decoding: Learning to Allocate Verification in Speculative Decoding — Hybrid Verified Decoding optimizes LLM inference by dynamically selecting between cache and model-based drafting based on predicted verification payoff.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.