# LLM Inference Benchmarking - Measure What Matters

DevFeed: [LLM Inference Benchmarking - Measure What Matters](<https://devfeed.tech/articles/llm-inference-benchmarking-measure-what-matters-19901.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/llm-inference-benchmarking>)

Author: Rithish Ramesh

Published: 2026-02-06T14:46:06Z

Content type: article

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Concurrency](<https://devfeed.tech/topics/concurrency.md>), [Cache](<https://devfeed.tech/topics/cache.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [cache](<https://devfeed.tech/tags/cache.md>), [compute](<https://devfeed.tech/tags/compute.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm](<https://devfeed.tech/tags/llm.md>), [moe](<https://devfeed.tech/tags/moe.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [performance](<https://devfeed.tech/tags/performance.md>), [routing](<https://devfeed.tech/tags/routing.md>)

## AI overview

This article examines LLM inference benchmarking as a complex systems problem spanning hardware and software. It focuses on how latency, throughput, concurrency, and cost interact, and distinguishes the compute-bound prefill phase from the memory-bound decode phase.

## Source excerpt

Production-grade LLM inference is a complex systems challenge, requiring deep co-designs - from hardware primitives (FLOPs, memory bandwidth, and interconnects) to sophisticated software layers - across the entire stack. Given the hardware variability across GPU providers like NVIDIA and AMD - including generational differences in numeric type performance (FP8, BF16, NVFP4 etc), HBM bandwidth and capacity, peak FLOPs etc - optimal performance is never guaranteed. It depends on the software's ability to maximize FLOPs utilization during prefill, maximize bandwidth efficiency during decode, optimize expert routing in MoE models, discover optimal parallelism strategies, and more. As inference hardware costs remain high, squeezing maximum performance to improve unit economics is a primary objective for AI teams. We are currently in an era of intense hardware-software co-design that will redefine performance and cost efficiency. Consequently, benchmarking must evolve to track three critical pillars: end-to-end model performance, micro-benchmarking of isolated components and a structured way to go after performance improvements. This article focuses on the LLM performance domain and analyzes the interplay between latency, throughput, concurrency, and cost. Prefill and Decode: The two phases of Inference LLM Inference works in two-phases: prefill and decode. The prefill phase is where the entire input goes through the model's forward pass which includes self-attention, add & norm, and pass through the hidden layers of the model's feed forward network. This phase is extremely compute bound. FLOPs per byte transferred (arithmetic intensity) for the prefill phase is very high. In simpler terms, the GPU is spending more time computing than waiting for the data from memory. On the other hand, the decode phase is memory bound. For every token that is generated, decode needs to load the entire weight matrix, KV cache from the HBM, generate one token, and write it back to the HBM