aiqbench analysis
Inference optimization is a practical concern for anyone running LLMs at scale, and this guide's main contribution is a framework for matching each technique (quantization, KV-cache management, scheduling, disaggregation) to the bottleneck it actually addresses. The useful takeaway is that the right fix depends on whether memory, compute, or request scheduling is limiting throughput, so diagnosing that first avoids unnecessary complexity.
Choosing an LLM Inference Optimization Technique: A Decision-Tree Approach
In this article, you will learn how to match an LLM inference optimization technique to the actual bottleneck in your workload by working through a...
In this article, you will learn how to match an LLM inference optimization technique to the actual bottleneck in your workload by working through a six-question decision tree. What each major inference optimization technique, from quantization to prefill-decode disaggregation, assumes about your workload. How to work through a decision tree that routes you to the right technique based on your model and traffic. How the techniques compare on trade-offs, and which symptoms show that a chosen technique isn’t working. LLM inference gets slow and expensive as traffic grows. Requests queue behind long generations, GPU memory fills with cached attention state, and the hardware spends much…
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