--- title: Develop Native Multimodal Agents with Qwen3.5 VLM Using... description: Alibaba's Qwen3.5 is a ~400B parameter open-source vision-language model (VLM) built for native multimodal agents, using a hybrid MoE and Gated Delta Networks... canonical: https://daily.dev/posts/develop-native-multimodal-agents-with-qwen3-5-vlm-using-nvidia-gpu-accelerated-endpoints-6vdfirboh twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Develop Native Multimodal Agents with Qwen3.5 VLM Using NVIDIA GPU-Accelerated Endpoints | daily.dev og:description: Alibaba's Qwen3.5 is a ~400B parameter open-source vision-language model (VLM) built for native multimodal agents, using a hybrid MoE and Gated Delta Networks... og:url: https://daily.dev/posts/develop-native-multimodal-agents-with-qwen3-5-vlm-using-nvidia-gpu-accelerated-endpoints-6vdfirboh og:image: https://api.daily.dev/og/posts/6VdFiRBoH.png og:image:alt: Develop Native Multimodal Agents with Qwen3.5 VLM Using NVIDIA GPU-Accelerated Endpoints 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. # Develop Native Multimodal Agents with Qwen3.5 VLM Using NVIDIA GPU-Accelerated Endpoints **[NVIDIA Developer](https://daily.dev/sources/nvidiadev)** · 3 min read · 1 upvotes · 0 comments ## Summary Alibaba's Qwen3.5 is a ~400B parameter open-source vision-language model (VLM) built for native multimodal agents, using a hybrid MoE and Gated Delta Networks architecture with 17B active parameters and a 256K context window extensible to 1M tokens. It supports UI navigation, visual reasoning, coding, and complex search across 200+ languages. NVIDIA provides GPU-accelerated endpoints via build.nvidia.com (powered by Blackwell GPUs) with free API access through the NVIDIA Developer Program. NVIDIA NIM enables containerized production deployment, while the NeMo Automodel library supports fine-tuning via SFT or LoRA, including a Medical Visual QA reference tutorial on radiological datasets. Multinode Slurm and Kubernetes deployments are supported for large-scale MoE workloads. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Run Multimodal Reasoning Agents with NVIDIA Nemotron on vLLM](https://daily.dev/posts/run-multimodal-reasoning-agents-with-nvidia-nemotron-on-vllm-vumht6rln) · vLLM · 0 upvotes · 0 comments - [Build with Kimi K2.5 Multimodal VLM Using NVIDIA GPU-Accelerated Endpoints](https://daily.dev/posts/build-with-kimi-k2-5-multimodal-vlm-using-nvidia-gpu-accelerated-endpoints-xrcpogwh8) · NVIDIA Developer · 31 upvotes · 0 comments - [Getting Started with Qwen3.5 Vision-Language Models](https://daily.dev/posts/getting-started-with-qwen3-5-vision-language-models-jolnfokep) · DigitalOcean Community · 1 upvotes · 0 comments - [Inside the vLLM-Omni architecture: Serving Qwen3-Omni](https://daily.dev/posts/inside-the-vllm-omni-architecture-serving-qwen3-omni-o8bxkmvxf) · Red Hat Developer · 0 upvotes · 0 comments --- Tags: [#agentic-ai](https://daily.dev/tags/agentic-ai), [#mixture-of-experts](https://daily.dev/tags/mixture-of-experts), [#qwen](https://daily.dev/tags/qwen), [#vlm](https://daily.dev/tags/vlm) [View this post on daily.dev](https://daily.dev/posts/develop-native-multimodal-agents-with-qwen3-5-vlm-using-nvidia-gpu-accelerated-endpoints-6vdfirboh) ```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":"Develop Native Multimodal Agents with Qwen3.5 VLM Using NVIDIA GPU-Accelerated Endpoints","url":"https://daily.dev/posts/develop-native-multimodal-agents-with-qwen3-5-vlm-using-nvidia-gpu-accelerated-endpoints-6vdfirboh","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/develop-native-multimodal-agents-with-qwen3-5-vlm-using-nvidia-gpu-accelerated-endpoints-6vdfirboh"},"datePublished":"2026-02-27T17:36:06.931Z","dateModified":"2026-04-30T02:18:35.245Z","description":"Alibaba's Qwen3.5 is a ~400B parameter open-source vision-language model (VLM) built for native multimodal agents, using a hybrid MoE and Gated Delta Networks...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/222ee3b18ad86755483d2083f7c0112e?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/222ee3b18ad86755483d2083f7c0112e?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"NVIDIA Developer","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"NVIDIA Developer","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/86e45aab42ba48ce83103d01b1119910","url":"https://daily.dev/sources/nvidiadev"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/develop-native-multimodal-agents-with-qwen3-5-vlm-using-nvidia-gpu-accelerated-endpoints-6vdfirboh","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"agentic-ai,mixture-of-experts,qwen,vlm","timeRequired":"PT3M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"NVIDIA Developer","item":"https://daily.dev/sources/nvidiadev"},{"@type":"ListItem","position":3,"name":"Develop Native Multimodal Agents with Qwen3.5 VLM Using NVIDIA GPU-Accelerated Endpoints"}]} ```