{"id":4119,"date":"2026-09-14T10:56:25","date_gmt":"2026-09-14T10:56:25","guid":{"rendered":"https:\/\/i10xnews.kinsta.cloud\/llm-inference-optimization-vllm-tgi-tensorrt"},"modified":"2026-09-14T10:56:25","modified_gmt":"2026-09-14T10:56:25","slug":"llm-inference-optimization-vllm-tgi-tensorrt","status":"publish","type":"post","link":"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt","title":{"rendered":"LLM Inference Optimization: vLLM, TGI &#038; TensorRT-LLM"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">\u26a1 Quick Take<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Summary:<\/strong> The battleground for AI dominance has shifted from model training to inference optimization. What we&#8217;re seeing is a sharp race between open-source frameworks, proprietary tools, and new hardware mixes that blend different chip types.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>What happened:<\/strong> Serving stacks are splitting into specialized camps. Open-source options like <strong>vLLM<\/strong> and <strong>Hugging Face\u2019s TGI<\/strong> sit alongside <strong>TensorRT-LLM<\/strong>. At the same time, players like China Mobile Cloud are testing heterogeneous setups that pair GPUs with <strong>neuromorphic<\/strong> chips for token processing.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Why it matters now:<\/strong> Training eats capital once and done. Inference, though, runs as an ongoing cost. Engines that use continuous batching, <strong>PagedAttention<\/strong>, and <strong>speculative decoding<\/strong> effectively multiply available compute. Operators are reporting 2\u20134x gains in tokens per second without fresh hardware purchases.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Who is most affected:<\/strong> Platform engineers, MLOps teams, and CTOs who must decide between vendor lock-in (NVIDIA or AWS) and the heavier lift of open-source setups to hit their latency targets.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The under-reported angle:<\/strong> The real constraint ahead isn&#8217;t just GPU supply. Memory bandwidth and power limits are tightening. That explains the growing interest in <strong>KV cache<\/strong> compression and neuromorphic assistance. Software tweaks and alternative silicon are stepping in where the grid can&#8217;t stretch further.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83e\udde0 Deep Dive<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Generative AI has moved into its industrial stage. Foundation models still grab the headlines, yet the practical pressure sits on the factory floor: running LLM inference at volume. Token generation is memory-bound and heavy on compute. Teams scaling these systems used to hit OOM errors and sudden latency jumps the moment load rose. Now optimized serving engines, vLLM, Hugging Face TGI, and TensorRT-LLM among them, are changing the unit economics.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The key change is how memory itself is handled. The <strong>KV cache<\/strong> that stores conversational context used to fragment and cap concurrency. vLLM\u2019s <strong>PagedAttention<\/strong> virtualized that cache and enabled continuous batching. New prompts can slot in without waiting for a full batch to finish. Providers using this approach have cut cost per thousand tokens by roughly 60 percent in some cases.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Still, context windows keep growing from 32k toward 1M tokens. Software alone won&#8217;t carry the load indefinitely. NVIDIA is doubling down on TensorRT-LLM and Triton to keep enterprise work inside the CUDA world, with low-precision kernels like <strong>FP8<\/strong>. Frameworks such as <strong>Ray Serve<\/strong> focus on the orchestration layer, letting high-priority requests preempt background work in multi-tenant setups.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Energy limits are pushing architecture in new directions. China Mobile Cloud recently showed a heterogeneous stack that hands certain decoding and scheduling tasks to <strong>neuromorphic<\/strong> hardware. As data centers near gigawatt-scale power ceilings, the single-vendor GPU model is starting to crack. Pairing traditional accelerators with lower-power ASICs or neuromorphic chips looks like a practical response to those constraints.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Teams are also leaning on <strong>speculative decoding<\/strong> and aggressive quantization (AWQ, <strong>GPTQ<\/strong>) to fit large models into available VRAM. The companies that win on infrastructure will be those that balance high throughput, strict latency SLAs, and low power draw per token, not simply those holding the largest GPU fleet.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udcca Stakeholders &amp; Impact<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Stakeholder \/ Aspect<\/th><th>Impact<\/th><th>Insight<\/th><\/tr><\/thead><tbody><tr><td>AI \/ LLM Builders<\/td><td>High<\/td><td>Must optimize models for <strong>INT4<\/strong> or <strong>FP8<\/strong> and confirm support for <strong>PagedAttention<\/strong> plus <strong>speculative decoding<\/strong>.<\/td><\/tr><tr><td>Infra &amp; Cloud Ops<\/td><td>High<\/td><td>Moving from simple GPU allocation to managing multi-tenant SLAs, load balancing, and continuous batching pipelines.<\/td><\/tr><tr><td>Silicon &amp; HW Vendors<\/td><td>Significant<\/td><td>NVIDIA\u2019s software position faces pressure from frameworks that run across TPUs, NPUs, and neuromorphic chips.<\/td><\/tr><tr><td>Enterprise End-Users<\/td><td>Medium\u2013High<\/td><td>Lower per-token costs and better latency, though on-premise rollouts still need solid ML engineering to stay clear of OOM issues.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">\u270d\ufe0f About the analysis<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This independent review draws on current inference frameworks, vendor benchmarks, and academic work to outline the serving landscape. It is written for MLOps engineers, platform architects, and CTOs weighing hardware limits against latency goals and scaling needs.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udd2d i10x Perspective<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Fixation on GPU counts has hidden how quickly software optimizations are moving relative to new hardware cycles. As open-source stacks make advanced memory techniques widely available, the edge for large cloud providers will shift toward orchestration quality, multi-tenancy controls, and energy metrics. Over the next five to ten years, the blend of heterogeneous silicon and techniques like speculative decoding will change how much physical space AI requires. At that point, <strong>scaling intelligence becomes as much a scheduling challenge as a raw compute one.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Discover how vLLM, Hugging Face TGI, and TensorRT-LLM boost LLM inference with PagedAttention and speculative decoding. Cut costs up to 60% and handle growing context windows. Explore the guide.<\/p>\n","protected":false},"author":2,"featured_media":4117,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"paper_title":"","paper_url":"","pdf_url":"","doi":"","arxiv_id":"","authors":"","institutions":"","paper_date":"","venue":"","peer_reviewed":false,"code_url":"","data_url":"","funding_coi":"","figure_license":"","paper_license":"","replication":"","study_models":"","study_sample":"","footnotes":"","_yoast_wpseo_primary_category":""},"categories":[11,9,30],"tags":[],"companies":[44],"products":[],"class_list":["post-4119","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-infrastructure","category-models","category-nvidia","company-nvidia"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>LLM Inference Optimization: vLLM, TGI &amp; TensorRT-LLM - i10X News<\/title>\n<meta name=\"description\" content=\"Optimize LLM inference with vLLM, TGI, and TensorRT-LLM. Learn about PagedAttention and speculative decoding for faster, cheaper AI.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LLM Inference Optimization: vLLM, TGI &amp; TensorRT-LLM - i10X News\" \/>\n<meta property=\"og:description\" content=\"Optimize LLM inference with vLLM, TGI, and TensorRT-LLM. Learn about PagedAttention and speculative decoding for faster, cheaper AI.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt\" \/>\n<meta property=\"og:site_name\" content=\"i10X News\" \/>\n<meta property=\"article:published_time\" content=\"2026-09-14T10:56:25+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/llm-inference-optimization.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1344\" \/>\n\t<meta property=\"og:image:height\" content=\"768\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Christopher Ort\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@i10x_ai\" \/>\n<meta name=\"twitter:site\" content=\"@i10x_ai\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Christopher Ort\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"NewsArticle\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/llm-inference-optimization-vllm-tgi-tensorrt#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/llm-inference-optimization-vllm-tgi-tensorrt\"},\"author\":{\"name\":\"Christopher Ort\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#\\\/schema\\\/person\\\/d410bb7ee72febefca2c9c369939219a\"},\"headline\":\"LLM Inference Optimization: vLLM, TGI &#038; TensorRT-LLM\",\"datePublished\":\"2026-09-14T10:56:25+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/llm-inference-optimization-vllm-tgi-tensorrt\"},\"wordCount\":722,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/llm-inference-optimization-vllm-tgi-tensorrt#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/llm-inference-optimization.webp\",\"articleSection\":[\"Infrastructure\",\"Models\",\"Nvidia\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/i10x.ai\\\/news\\\/llm-inference-optimization-vllm-tgi-tensorrt#respond\"]}],\"dateModified\":\"2026-09-14T10:56:25+00:00\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/llm-inference-optimization-vllm-tgi-tensorrt\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\\\/llm-inference-optimization-vllm-tgi-tensorrt\",\"name\":\"LLM Inference Optimization: vLLM, TGI & TensorRT-LLM - i10X News\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/llm-inference-optimization-vllm-tgi-tensorrt#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/llm-inference-optimization-vllm-tgi-tensorrt#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/llm-inference-optimization.webp\",\"datePublished\":\"2026-09-14T10:56:25+00:00\",\"description\":\"Optimize LLM inference with vLLM, TGI, and TensorRT-LLM. Learn about PagedAttention and speculative decoding for faster, cheaper AI.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/llm-inference-optimization-vllm-tgi-tensorrt#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/i10x.ai\\\/news\\\/llm-inference-optimization-vllm-tgi-tensorrt\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/llm-inference-optimization-vllm-tgi-tensorrt#primaryimage\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/llm-inference-optimization.webp\",\"contentUrl\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/llm-inference-optimization.webp\",\"width\":1344,\"height\":768,\"caption\":\"Abstract AI architecture with a central processor labeled AI, connected to chips and data stacks, illustrating inference optimization.\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/llm-inference-optimization-vllm-tgi-tensorrt#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/i10x.ai\\\/news\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"LLM Inference Optimization: vLLM, TGI &#038; TensorRT-LLM\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#website\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\",\"name\":\"i10X News\",\"description\":\"Stay updated with the latest news and announcements from i10X.\",\"publisher\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/i10x.ai\\\/news?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#organization\",\"name\":\"i10X\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/i10xnews.kinsta.cloud\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/i10x-logo-180.png\",\"contentUrl\":\"https:\\\/\\\/i10xnews.kinsta.cloud\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/i10x-logo-180.png\",\"width\":180,\"height\":180,\"caption\":\"i10X\"},\"image\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/x.com\\\/i10x_ai\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/i10x-ai\",\"https:\\\/\\\/instagram.com\\\/i10x.ai\",\"https:\\\/\\\/www.youtube.com\\\/@i10X_ai_official\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#\\\/schema\\\/person\\\/d410bb7ee72febefca2c9c369939219a\",\"name\":\"Christopher Ort\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/christopher-ort-192.webp\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/christopher-ort-192.webp\",\"contentUrl\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/christopher-ort-192.webp\",\"caption\":\"Christopher Ort\"},\"description\":\"Christopher Ort handles SEO, AI agent workflows and PR & partnerships at i10x.ai, the all-in-one AI workspace built by i10X Global Pte. Ltd. in Singapore. He is a graduate of Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU). He is the author of \u201cThe Style Penalty\u201d, a study published on SSRN that analyses 1,576 evaluation decisions by four frontier AI models to show how AI-written CVs fare in automated hiring screens; the research has been covered by heise online and t3n.\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\\\/author\\\/christopher-ort\",\"sameAs\":[\"https:\\\/\\\/i10x.ai\\\/blog\\\/author\\\/christopher-ort\"],\"worksFor\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#organization\"},\"alumniOf\":{\"@type\":\"EducationalOrganization\",\"name\":\"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU)\",\"url\":\"https:\\\/\\\/www.fau.eu\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"LLM Inference Optimization: vLLM, TGI & TensorRT-LLM - i10X News","description":"Optimize LLM inference with vLLM, TGI, and TensorRT-LLM. Learn about PagedAttention and speculative decoding for faster, cheaper AI.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt","og_locale":"en_US","og_type":"article","og_title":"LLM Inference Optimization: vLLM, TGI & TensorRT-LLM - i10X News","og_description":"Optimize LLM inference with vLLM, TGI, and TensorRT-LLM. Learn about PagedAttention and speculative decoding for faster, cheaper AI.","og_url":"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt","og_site_name":"i10X News","article_published_time":"2026-09-14T10:56:25+00:00","og_image":[{"width":1344,"height":768,"url":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/llm-inference-optimization.webp","type":"image\/webp"}],"author":"Christopher Ort","twitter_card":"summary_large_image","twitter_creator":"@i10x_ai","twitter_site":"@i10x_ai","twitter_misc":{"Written by":"Christopher Ort","Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"NewsArticle","@id":"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt#article","isPartOf":{"@id":"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt"},"author":{"name":"Christopher Ort","@id":"https:\/\/i10x.ai\/news\/#\/schema\/person\/d410bb7ee72febefca2c9c369939219a"},"headline":"LLM Inference Optimization: vLLM, TGI &#038; TensorRT-LLM","datePublished":"2026-09-14T10:56:25+00:00","mainEntityOfPage":{"@id":"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt"},"wordCount":722,"commentCount":0,"publisher":{"@id":"https:\/\/i10x.ai\/news\/#organization"},"image":{"@id":"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt#primaryimage"},"thumbnailUrl":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/llm-inference-optimization.webp","articleSection":["Infrastructure","Models","Nvidia"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt#respond"]}],"dateModified":"2026-09-14T10:56:25+00:00"},{"@type":"WebPage","@id":"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt","url":"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt","name":"LLM Inference Optimization: vLLM, TGI & TensorRT-LLM - i10X News","isPartOf":{"@id":"https:\/\/i10x.ai\/news\/#website"},"primaryImageOfPage":{"@id":"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt#primaryimage"},"image":{"@id":"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt#primaryimage"},"thumbnailUrl":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/llm-inference-optimization.webp","datePublished":"2026-09-14T10:56:25+00:00","description":"Optimize LLM inference with vLLM, TGI, and TensorRT-LLM. Learn about PagedAttention and speculative decoding for faster, cheaper AI.","breadcrumb":{"@id":"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt#primaryimage","url":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/llm-inference-optimization.webp","contentUrl":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/llm-inference-optimization.webp","width":1344,"height":768,"caption":"Abstract AI architecture with a central processor labeled AI, connected to chips and data stacks, illustrating inference optimization."},{"@type":"BreadcrumbList","@id":"https:\/\/i10x.ai\/news\/llm-inference-optimization-vllm-tgi-tensorrt#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/i10x.ai\/news"},{"@type":"ListItem","position":2,"name":"LLM Inference Optimization: vLLM, TGI &#038; TensorRT-LLM"}]},{"@type":"WebSite","@id":"https:\/\/i10x.ai\/news\/#website","url":"https:\/\/i10x.ai\/news","name":"i10X News","description":"Stay updated with the latest news and announcements from i10X.","publisher":{"@id":"https:\/\/i10x.ai\/news\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/i10x.ai\/news?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/i10x.ai\/news\/#organization","name":"i10X","url":"https:\/\/i10x.ai\/news\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/i10x.ai\/news\/#\/schema\/logo\/image\/","url":"https:\/\/i10xnews.kinsta.cloud\/wp-content\/uploads\/2026\/10\/i10x-logo-180.png","contentUrl":"https:\/\/i10xnews.kinsta.cloud\/wp-content\/uploads\/2026\/10\/i10x-logo-180.png","width":180,"height":180,"caption":"i10X"},"image":{"@id":"https:\/\/i10x.ai\/news\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/x.com\/i10x_ai","https:\/\/www.linkedin.com\/company\/i10x-ai","https:\/\/instagram.com\/i10x.ai","https:\/\/www.youtube.com\/@i10X_ai_official"]},{"@type":"Person","@id":"https:\/\/i10x.ai\/news\/#\/schema\/person\/d410bb7ee72febefca2c9c369939219a","name":"Christopher Ort","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/christopher-ort-192.webp","url":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/christopher-ort-192.webp","contentUrl":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/christopher-ort-192.webp","caption":"Christopher Ort"},"description":"Christopher Ort handles SEO, AI agent workflows and PR & partnerships at i10x.ai, the all-in-one AI workspace built by i10X Global Pte. Ltd. in Singapore. He is a graduate of Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU). He is the author of \u201cThe Style Penalty\u201d, a study published on SSRN that analyses 1,576 evaluation decisions by four frontier AI models to show how AI-written CVs fare in automated hiring screens; the research has been covered by heise online and t3n.","url":"https:\/\/i10x.ai\/news\/author\/christopher-ort","sameAs":["https:\/\/i10x.ai\/blog\/author\/christopher-ort"],"worksFor":{"@id":"https:\/\/i10x.ai\/news\/#organization"},"alumniOf":{"@type":"EducationalOrganization","name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU)","url":"https:\/\/www.fau.eu"}}]}},"_links":{"self":[{"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/posts\/4119","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/comments?post=4119"}],"version-history":[{"count":1,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/posts\/4119\/revisions"}],"predecessor-version":[{"id":5692,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/posts\/4119\/revisions\/5692"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/media\/4117"}],"wp:attachment":[{"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/media?parent=4119"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/categories?post=4119"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/tags?post=4119"},{"taxonomy":"company","embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/companies?post=4119"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/products?post=4119"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}