Benchmarking LLM Inference Clusters for AI Teams

If you’re an AI engineer trying to optimize your LLMs for inference, here’s a quick guide for you 👇 Efficient inference isn’t just about faster hardware, it’s a multi-layered design problem. From how you compress prompts to how your memory is managed across GPUs, everything impacts latency, throughput, and cost. Here’s a structured taxonomy of inference-time optimizations for LLMs: 1. Data-Level Optimization Reduce redundant tokens and unnecessary output computation. → Input Compression:  - Prompt Pruning, remove irrelevant history or system tokens  - Prompt Summarization, use model-generated summaries as input  - Soft Prompt Compression, encode static context using embeddings  - RAG, replace long prompts with retrieved documents plus compact queries → Output Organization:  - Pre-structure output to reduce decoding time and minimize sampling steps 2. Model-Level Optimization (a) Efficient Structure Design → Efficient FFN Design, use gated or sparsely-activated FFNs (e.g., SwiGLU) → Efficient Attention, FlashAttention, linear attention, or sliding window for long context → Transformer Alternates, e.g., Mamba, Reformer for memory-efficient decoding → Multi/Group-Query Attention, share keys/values across heads to reduce KV cache size → Low-Complexity Attention, replace full softmax with approximations (e.g., Linformer) (b) Model Compression → Quantization:  - Post-Training, no retraining needed  - Quantization-Aware Training, better accuracy, especially <8-bit → Sparsification:  - Weight Pruning, Sparse Attention → Structure Optimization:  - Neural Architecture Search, Structure Factorization → Knowledge Distillation:  - White-box, student learns internal states  - Black-box, student mimics output logits → Dynamic Inference, adaptive early exits or skipping blocks based on input complexity 3. System-Level Optimization (a) Inference Engine → Graph & Operator Optimization, use ONNX, TensorRT, BetterTransformer for op fusion → Speculative Decoding, use a smaller model to draft tokens, validate with full model → Memory Management, KV cache reuse, paging strategies (e.g., PagedAttention in vLLM) (b) Serving System → Batching, group requests with similar lengths for throughput gains → Scheduling, token-level preemption (e.g., TGI, vLLM schedulers) → Distributed Systems, use tensor, pipeline, or model parallelism to scale across GPUs My Two Cents 🫰 → Always benchmark end-to-end latency, not just token decode speed → For production, 8-bit or 4-bit quantized models with MQA and PagedAttention give the best price/performance → If using long context (>64k), consider sliding attention plus RAG, not full dense memory → Use speculative decoding and batching for chat applications with high concurrency → LLM inference is a systems problem. Optimizing it requires thinking holistically, from tokens to tensors to threads. Image inspo: A Survey on Efficient Inference for Large Language Models ---- Follow me (Aishwarya Srinivasan) for more AI insights!

𝗠𝗟𝗣𝗲𝗿𝗳 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝘃𝟲.𝟬 𝗶𝘀 𝗮 𝘀𝘁𝗿𝗼𝗻𝗴 𝘀𝗶𝗴𝗻𝗮𝗹 𝗳𝗼𝗿 𝘄𝗵𝗲𝗿𝗲 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗟𝗟𝗠 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗶𝘀 𝗵𝗲𝗮𝗱𝗶𝗻𝗴. The latest results from Dell Technologies with AMD Instinct #MI355X stand out on a few fronts:  • 𝟰× 𝗴��𝗻-𝗼𝘃𝗲𝗿-𝗴𝗲𝗻 𝘂𝗽𝗹𝗶𝗳𝘁 𝗼𝗻 𝗟𝗹𝗮𝗺𝗮𝟮-𝟳𝟬𝗕 with the new #PowerEdge_XE9785L (8× MI355X) vs. prior MI300X systems... enough to change how we think about capacity planning, model size, and consolidation of inference workloads.  • 𝗙𝗶𝗿𝘀𝘁 𝗠𝗟𝗣𝗲𝗿𝗳 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗼𝗽𝗲𝗻 𝗚𝗣𝗧-𝗢𝗦𝗦-𝟭𝟮𝟬𝗕 𝗺𝗼𝗱𝗲𝗹, showing that 100B+ parameter, open LLMs can deliver enterprise-scale throughput on this platform.. important for organizations pursuing open or #sovereign_AI strategies.  • 𝗡𝗲𝗮𝗿-𝗽𝗲𝗿𝗳𝗲𝗰𝘁 (~𝟵𝟱.𝟱%) 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗮𝗰𝗿𝗼𝘀𝘀 𝗮 𝗺𝗶𝘅𝗲𝗱, 𝗺𝘂𝗹𝘁𝗶𝗿𝗲𝗴𝗶𝗼𝗻 𝗚𝗣𝗨 𝗰𝗹𝘂𝘀𝘁𝗲𝗿 (MI300X, MI325X, MI355X across US and Korea) using MangoBoost’s LLMBoost, demonstrating that you can modernize heterogeneous estates without a full network or cluster redesign.  • 𝗠𝗲𝗮𝗻𝗶𝗻𝗴𝗳𝘂𝗹 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗮𝘁 𝗮 𝟭𝟬𝟬𝟬𝗪 𝗽𝗼𝘄𝗲𝗿 𝗰𝗮𝗽 on MI355X, with only ~16–18% throughput reduction for a 29% power drop and a ~17.5% improvement in tokens/s per Watt... critical for power-constrained or sustainability-focused data centers. WHY CARE?: These results show that enterprises can 𝘀𝗰𝗮𝗹𝗲 𝗟𝗟𝗠 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗴𝗹𝗼𝗯𝗮𝗹𝗹𝘆, 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗻𝗲𝘄 𝗔𝗜 𝘀𝗲𝗿𝘃𝗲𝗿𝘀 𝗶𝗻𝘁𝗼 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀, 𝗮𝗻𝗱 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗳𝗼𝗿 𝗽𝗼𝘄𝗲𝗿 𝗮𝗻𝗱 𝗧𝗖𝗢, 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗼𝗺𝗽𝗿𝗼𝗺𝗶𝘀𝗶𝗻𝗴 𝗼𝗻 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲. 𝗙𝘂𝗹𝗹 test results:  https://lnkd.in/gGhGZ6XH #IWork4Dell, #MLPerf MLCommons, Frank Han, Will LaForge, Mike Darby

🚀 Before you launch your LLM into production, it’s essential to understand how your inference endpoints perform under load. In this latest blog post, Gary Stafford explores load testing Amazon Web Services (AWS) SageMaker real-time inference endpoints using Locust, an open-source tool for simulating user demand at scale. Discover how model size, instance type, hosting framework, deployment configuration, and inference parameters impact peak requests-per-second (RPS) and latency—key metrics for delivering reliable and performant AI applications. 🔍 Learn how to: • Benchmark your SageMaker endpoints under load • Identify performance bottlenecks before they impact users • Optimize your deployment for scalability and responsiveness Whether you’re deploying new LLM features or scaling existing production workloads, this guide will show you how to optimize the performance of your inference endpoints and make data-driven infrastructure decisions. All open-source code is available on GitHub. #AWS #SageMaker #LLM #LoadTesting #Locust #MachineLearning #AI #PerformanceTesting

We served 980 trillion tokens in June. That's a whole lot of applications built, deep research done, Veo videos generated, old photos restored, reports summarized, and so on... What's cool on the infrastructure side is that we learn from all those use cases, and build new inference capabilities for our Cloud customers. Analyzing thousands of lines of code? We better be bringing more processing to the workload, spanning hosts as needed. Handling thousands of interactive chats simultaneously? We better be smarter about finding underutilized inference servers and reusing precalculated values to handle repetitive prompts. That's why we've been introducing new capabilities throughout our AI infrastructure, from smarter load balancing and routing with our Inference Gateway, to faster data loading in our Cloud Storage, to more accelerator options for common inference engines like vLLM. These can improve throughput (with disaggregated serving) by 60% and TTFT latency by up to 96% at peak throughput. And we're making the optimization of these components with the ever changing model landscape easier with Inference Quickstarts and reproducible benchmark recipes. Inference Quickstarts is benchmarking over 100 combinations of models and infrastructure every week so you don't have to! Best of all? Many of these new capabilities don't cost anything. 🤩 To learn more: - Inference Gateway GA https://lnkd.in/gDS3CYWY - Inference Quickstart https://lnkd.in/gfGbYV7g - Recipes for Llama 4 and Deepseek https://lnkd.in/ggka4uZW - Recipe for NVIDIA Dynamo https://lnkd.in/gVdyQXZx Mark George Gurmeet Nirav Mohan Nathan Akshay Drew Roy Amin Moritz Masha Vyacheslav (Slava) Niamh Flora Rajat Daniel Guillaume Jamie

Inference is now the dominant AI workload. Serving LLMs at scale is a systems test: tokens, tail latency, and concurrency... not peak FLOPS. I’m excited by what we’re seeing with MI355X GPUs in production inference. In this DriveNets configuration on DeepSeek-R1 (FP8, SGLang), at concurrency 64: • +12% throughput per GPU vs B200 • 22% faster token streaming (lower TPOT) • ~28 vs 22 tokens/sec/user under load On interactive runs, P99 TTFT stayed < 1s as concurrency climbed... no tail-latency blowups. https://lnkd.in/gedqJCy6 At 72-GPU cluster scale, disaggregated prefill/decode remained stable and tunable. This is a full-stack win: GPU + memory + fabric + NIC + software, validated end-to-end. Inference is no longer a synthetic benchmark exercise. It is an operational discipline.