--- title: ModelExpress: Distributing Model Artifacts at the Speed... description: NVIDIA ModelExpress (MX) is a system for accelerating model weight distribution across GPU clusters. Instead of treating every replica as a cold start, MX... canonical: https://daily.dev/posts/modelexpress-distributing-model-artifacts-at-the-speed-of-light-xhp0tx8td twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: ModelExpress: Distributing Model Artifacts at the Speed of Light | daily.dev og:description: NVIDIA ModelExpress (MX) is a system for accelerating model weight distribution across GPU clusters. Instead of treating every replica as a cold start, MX... og:url: https://daily.dev/posts/modelexpress-distributing-model-artifacts-at-the-speed-of-light-xhp0tx8td og:image: https://api.daily.dev/og/posts/xhP0Tx8td.png og:image:alt: ModelExpress: Distributing Model Artifacts at the Speed of Light og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # ModelExpress: Distributing Model Artifacts at the Speed of Light **[NVIDIA Developer](https://daily.dev/sources/nvidiadev)** · 11 min read · 0 upvotes · 0 comments ## Summary NVIDIA ModelExpress (MX) is a system for accelerating model weight distribution across GPU clusters. Instead of treating every replica as a cold start, MX selects the fastest available weight source: P2P RDMA from a serving peer via NIXL, streaming from object storage without touching disk, or GPUDirect Storage. For DeepSeek-V4 Pro on 8xB200 nodes, MX reduces startup time from 8 minutes to under 2 minutes. Beyond weights, MX transfers JIT kernel caches (Triton, DeepGEMM, FlashInfer) between replicas to eliminate repeated warmup compilation. For RL post-training, MX supports receiver-driven weight refit from trainer shards directly into inference engine layout. The system integrates with vLLM, SGLang, and Dynamo, and is open source under the ai-dynamo project. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Delivering Flexible Performance for Future-Ready Data Centers with NVIDIA MGX](https://daily.dev/posts/delivering-flexible-performance-for-future-ready-data-centers-with-nvidia-mgx-22di7x9jp) · NVIDIA Developer · 1 upvotes · 0 comments - [Accelerate LLM model loading and increase context windows with GPUDirect on Amazon FSx for Lustre and TurboQuant](https://daily.dev/posts/accelerate-llm-model-loading-and-increase-context-windows-with-gpudirect-on-amazon-fsx-for-lustre-an-u0pcfjh9q) · AWS · 0 upvotes · 0 comments - [Mixture of Experts Powers the Most Intelligent Frontier AI Models, Runs 10x Faster on NVIDIA Blackwell NVL72](https://daily.dev/posts/mixture-of-experts-powers-the-most-intelligent-frontier-ai-models-runs-10x-faster-on-nvidia-blackwe-mlfwpdcyw) · NVIDIA · 0 upvotes · 0 comments - [Optimizing Data Transfer in Distributed AI/ML Training Workloads](https://daily.dev/posts/optimizing-data-transfer-in-distributed-ai-ml-training-workloads-c015cc2oy) · Towards Data Science · 0 upvotes · 0 comments --- Tags: [#nvidia](https://daily.dev/tags/nvidia), [#ai-inference](https://daily.dev/tags/ai-inference), [#vllm](https://daily.dev/tags/vllm) [View this post on daily.dev](https://daily.dev/posts/modelexpress-distributing-model-artifacts-at-the-speed-of-light-xhp0tx8td) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"ModelExpress: Distributing Model Artifacts at the Speed of Light","url":"https://daily.dev/posts/modelexpress-distributing-model-artifacts-at-the-speed-of-light-xhp0tx8td","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/modelexpress-distributing-model-artifacts-at-the-speed-of-light-xhp0tx8td"},"datePublished":"2026-07-24T16:50:16.658Z","dateModified":"2026-07-24T16:56:35.470Z","description":"NVIDIA ModelExpress (MX) is a system for accelerating model weight distribution across GPU clusters. 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