{"id":3934,"date":"2026-05-01T11:23:32","date_gmt":"2026-05-01T11:23:32","guid":{"rendered":"https:\/\/cloudobjectivity.co.uk\/?p=3934"},"modified":"2026-05-04T16:56:18","modified_gmt":"2026-05-04T16:56:18","slug":"how-many-users-can-your-llm-server-really-handle","status":"publish","type":"post","link":"https:\/\/cloudobjectivity.co.uk\/index.php\/2026\/05\/01\/how-many-users-can-your-llm-server-really-handle\/","title":{"rendered":"How Many Users Can Your LLM Server Really Handle?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3934\" class=\"elementor elementor-3934\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3985d952 e-flex e-con-boxed e-con e-parent\" data-id=\"3985d952\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1e5377a2 elementor-widget elementor-widget-text-editor\" data-id=\"1e5377a2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t\n<p><\/p>\n\n\n\n<p><strong>Executive Overview<\/strong><\/p>\n\n\n\n<p>As Large Language Models (LLMs) move from experimental labs to mission-critical production, predicting the capacity of inference servers has become a complex engineering challenge. This article introduces <strong>SPOC<\/strong> (Stateful, Profile-based Optimization for LLM Capacity Planning), a rigorous methodology designed to move beyond simple &#8220;leaderboard&#8221; benchmarks. It focuses on how infrastructure teams can systematically measure and optimize LLM performance on NVIDIA H100 and H200 clusters within VMware Cloud Foundation environments.<\/p>\n\n\n\n<p><strong>Features<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Stateful Workload Modeling:<\/strong> Uses the Locust framework to simulate real-world, multi-turn developer conversations rather than stateless, single-turn prompts.<\/li>\n\n\n\n<li><strong>Evolutionary Parameter Search:<\/strong> Employs the <strong>Optuna NSGA-II<\/strong> algorithm to mathematically navigate vLLM settings (like <code>--max-num-batched-tokens<\/code>) to find the &#8220;Pareto front&#8221; of throughput vs. latency.<\/li>\n\n\n\n<li><strong>Kernel-Level Profiling:<\/strong> Integration with <strong>NVIDIA Nsight Systems<\/strong> to identify architectural bottlenecks (e.g., Attention kernels vs. memory bandwidth).<\/li>\n\n\n\n<li><strong>Advanced Telemetry:<\/strong> Uses Prometheus and DCGM Exporter to correlate software-level inference metrics with physical hardware health.<\/li>\n<\/ul>\n\n\n\n<p><strong>Benefits<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Predictable SLAs:<\/strong> Provides a blueprint for maintaining strict Service Level Agreements (SLAs) for Time-to-First-Token (TTFT) and Inter-Token Latency (ITL).<\/li>\n\n\n\n<li><strong>Optimized Resource Allocation:<\/strong> Demonstrates how to extract maximum performance from expensive GPU assets without over-provisioning.<\/li>\n\n\n\n<li><strong>Tail Latency Protection:<\/strong> Introduces &#8220;Chunked Prefill&#8221; strategies to prevent large requests from causing latency spikes for other users.<\/li>\n\n\n\n<li><strong>Operational Transparency:<\/strong> Reveals hidden risks like &#8220;thermal throttling&#8221; in Tensor Parallelism, where one overheating GPU can slow down an entire cluster.<\/li>\n<\/ul>\n\n\n\n<p><strong>Use Cases<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Enterprise AI Coding Assistants:<\/strong> Planning infrastructure for developers querying massive internal monorepos.<\/li>\n\n\n\n<li><strong>Log Analysis &amp; Troubleshooting:<\/strong> Handling heavy bursts of data where context sizes can reach up to 128k tokens.<\/li>\n\n\n\n<li><strong>Internal Chatbots:<\/strong> Balancing high-concurrency short requests with fewer, high-complexity long-context queries.<\/li>\n<\/ul>\n\n\n\n<p><strong>Alternatives<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Standard Synthetic Benchmarking:<\/strong> Using tools like MLPerf or GenAI Perf, which the authors argue are insufficient for capacity planning because they use &#8220;average&#8221; request sizes.<\/li>\n\n\n\n<li><strong>Manual Tuning:<\/strong> Relying on &#8220;best guess&#8221; heuristics for vLLM parameters, which often leads to sub-optimal performance or SLA violations.<\/li>\n<\/ul>\n\n\n\n<p><strong>Alternative Perspective<\/strong><\/p>\n\n\n\n<p>While the SPOC methodology offers extreme precision, it requires a high level of expertise in data science and performance engineering. Small to medium-sized enterprises might find the overhead of setting up multi-objective evolutionary algorithms and kernel-level tracing too resource-intensive compared to slightly over-provisioning their environment or using managed AI services.<\/p>\n\n\n\n<p><strong>Final Thoughts<\/strong><\/p>\n\n\n\n<p>This article signals a shift in the AI industry from &#8220;just getting it to work&#8221; to &#8220;industrial-scale optimization.&#8221; By treating LLM capacity as a multi-objective math problem, VCF enables enterprises to run Private AI with the same efficiency and predictability as traditional virtualized workloads.<\/p>\n\n\n\n<p><strong>Source<\/strong><\/p>\n\n\n\n<p><a target=\"_blank\" rel=\"noreferrer noopener\" href=\"https:\/\/blogs.vmware.com\/cloud-foundation\/2026\/04\/30\/how-many-users-can-your-llm-server-really-handle\/\">How Many Users Can Your LLM Server Really Handle?<\/a> (Published: April 30, 2026)<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Executive Overview As Large Language Models (LLMs) move from experimental labs to mission-critical production, predicting the capacity of inference servers has become a complex engineering challenge. This article introduces SPOC (Stateful, Profile-based Optimization for LLM Capacity Planning), a rigorous methodology designed to move beyond simple &#8220;leaderboard&#8221; benchmarks. It focuses on how infrastructure teams can systematically [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"footnotes":""},"categories":[21,20],"tags":[25,53,52,34],"class_list":["post-3934","post","type-post","status-publish","format-standard","hentry","category-ai","category-vmware-news","tag-ai","tag-vcf","tag-vmware","tag-vmware-news"],"_links":{"self":[{"href":"https:\/\/cloudobjectivity.co.uk\/index.php\/wp-json\/wp\/v2\/posts\/3934","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cloudobjectivity.co.uk\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cloudobjectivity.co.uk\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cloudobjectivity.co.uk\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/cloudobjectivity.co.uk\/index.php\/wp-json\/wp\/v2\/comments?post=3934"}],"version-history":[{"count":4,"href":"https:\/\/cloudobjectivity.co.uk\/index.php\/wp-json\/wp\/v2\/posts\/3934\/revisions"}],"predecessor-version":[{"id":3938,"href":"https:\/\/cloudobjectivity.co.uk\/index.php\/wp-json\/wp\/v2\/posts\/3934\/revisions\/3938"}],"wp:attachment":[{"href":"https:\/\/cloudobjectivity.co.uk\/index.php\/wp-json\/wp\/v2\/media?parent=3934"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cloudobjectivity.co.uk\/index.php\/wp-json\/wp\/v2\/categories?post=3934"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cloudobjectivity.co.uk\/index.php\/wp-json\/wp\/v2\/tags?post=3934"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}