Inference Optimization — How to Make LLMs Faster and Cheaper in Production

2026-05-18 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Advanced, long

Summary

LLM inference optimization is a critical engineering discipline that significantly reduces the cost and latency of deploying large language models in production. Techniques such as continuous batching, PagedAttention, speculative decoding, and quantization (INT8/INT4) can collectively improve throughput by 5-20x and latency by 2-10x without compromising model quality. Continuous batching, implemented in frameworks like vLLM and TGI, boosts throughput by 3-10x by dynamically replacing completed requests in a batch. PagedAttention, a vLLM innovation, optimizes KV cache memory utilization by treating it like virtual memory, enabling 2-5x more concurrent requests. Speculative decoding uses a smaller draft model to propose tokens, which a larger target model verifies in parallel, yielding 2-3x throughput gains for predictable outputs. Quantization to INT8 or INT4 reduces memory footprint by 2-4x with minimal quality degradation, while FlashAttention and fused kernels enhance speed by optimizing hardware-level computations. Additionally, prompt caching reuses KV states for common prefixes, cutting costs by 60-80% for repeated requests, and intelligent request routing directs queries to the most appropriate model, reducing average inference costs by 3-5x.

Key takeaway

For MLOps Engineers deploying LLMs, prioritizing inference optimization is crucial for economic viability. You should start by implementing continuous batching with a dedicated serving framework like vLLM or TGI to achieve immediate 5-10x throughput gains. Additionally, quantizing your models to INT8 is a low-risk, high-reward step that typically reduces memory by 2x and increases throughput by 1.3-1.5x with minimal quality loss. Always measure Time to First Token (TTFT) and throughput independently, as they require distinct optimization strategies.

Key insights

Optimizing LLM inference in production requires a stack of techniques to balance latency, throughput, and cost.

Principles

• GPU utilization drives cost efficiency.
• Memory access is a key bottleneck.
• Batching improves throughput significantly.

Method

Combine continuous batching, PagedAttention, speculative decoding, quantization, FlashAttention, and prompt caching within a robust serving framework like vLLM or TGI to achieve optimal LLM inference performance.

In practice

• Implement continuous batching with vLLM or TGI.
• Quantize models to INT8 for 2x memory reduction.
• Measure TTFT and throughput separately.

Topics

• LLM Inference Optimization
• Continuous Batching
• PagedAttention
• Speculative Decoding
• Quantization

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

Related on AIssential

• 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.
• Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient — Continuous batching optimizes LLM inference by dynamically scheduling requests and packing tokens to eliminate padding and idle GPU time.
• Optimize, deploy, and benchmark an open-source LLM with vLLM — Efficient LLM inference is achieved through specialized memory management and serving systems like vLLM.
• How To Reduce Inference Costs While Running LLMs — Optimizing LLM inference costs and performance requires a multi-faceted approach across hardware, software, and architectural techniques.
• From LLMs to Products: Alignment & Production — Transforming base LLMs into reliable, cost-effective production systems requires both alignment and deployment optimization.
• Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints — Fluid-guided online scheduling optimizes LLM inference by managing KV cache memory growth to reduce latency and expand stable operation.

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.