{"id":1973,"date":"2026-08-24T12:34:27","date_gmt":"2026-08-24T12:34:27","guid":{"rendered":"https:\/\/blog.openzeka.com\/en\/?p=1973"},"modified":"2026-08-24T12:35:19","modified_gmt":"2026-08-24T12:35:19","slug":"nvidia-dgx-spark-ecosystem","status":"publish","type":"post","link":"https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/","title":{"rendered":"NVIDIA DGX Spark Ecosystem"},"content":{"rendered":"<div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 fusion-flex-container has-pattern-background has-mask-background nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:1331.2px;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-bg-size:cover;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:1.92%;--awb-margin-bottom-large:20px;--awb-spacing-left-large:1.92%;--awb-width-medium:100%;--awb-order-medium:0;--awb-spacing-right-medium:1.92%;--awb-spacing-left-medium:1.92%;--awb-width-small:100%;--awb-order-small:0;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-text fusion-text-1\"><h2>Resources, Tools, and Community<\/h2>\n<\/div><div class=\"fusion-text fusion-text-2\"><p><strong>NVIDIA DGX Spark\u2122<\/strong> is a compact desktop AI system designed for working with artificial intelligence models. It brings development, testing, inference, fine-tuning, and prototyping workflows together on a single platform.<\/p>\n<\/div>    <div class=\"ext-wc-slider-wrapper\">\n        <div class=\"ext-wc-slider-header\">\n            <h4 class=\"ext-wc-slider-heading\">Recommended Products<\/h4>\n            <a href=\"https:\/\/openzeka.com\/en\/store\/?orderby=menu_order\" target=\"_blank\" rel=\"noopener\" class=\"ext-wc-all-link\">\n                See All Products &rarr; \n                <svg width=\"16\" height=\"16\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\">\n                    <circle cx=\"9\" cy=\"21\" r=\"1\"><\/circle>\n                    <circle cx=\"20\" cy=\"21\" r=\"1\"><\/circle>\n                    <path d=\"M1 1h4l2.68 13.39a2 2 0 0 0 2 1.61h9.72a2 2 0 0 0 2-1.61L23 6H6\"><\/path>\n                <\/svg>\n            <\/a>\n        <\/div>\n        \n        <div class=\"ext-wc-slider-rel-container\">\n            <button class=\"ext-wc-arrow ext-wc-arrow-prev\" aria-label=\"\u00d6nceki\">&#10094;<\/button>\n            \n            <div class=\"ext-wc-slider-container\">\n                                    <div class=\"ext-wc-slider-item\">\n                        <a href=\"https:\/\/openzeka.com\/en\/product\/nvidia-dgx-spark-bundle\/\" target=\"_blank\" rel=\"nofollow noopener\">\n                            <div class=\"ext-wc-slider-content\">\n                                                                    <div class=\"ext-wc-slider-img\">\n                                        <img decoding=\"async\" src=\"https:\/\/openzeka.com\/en\/wp-content\/uploads\/2026\/05\/DGX-Spark-img-6.png\" alt=\"NVIDIA DGX Spark Bundle\">\n                                    <\/div>\n                                \n                                <div class=\"ext-wc-slider-info\">\n                                                                            <div class=\"ext-wc-slider-category\">DGX Systems<\/div>\n                                    \n                                    <div class=\"ext-wc-slider-title\">NVIDIA DGX Spark Bundle<\/div>\n\n                                    <div class=\"ext-wc-slider-btn\">\u0130ncele<\/div>\n                                <\/div>\n                            <\/div>\n                        <\/a>\n                    <\/div>\n                                    <div class=\"ext-wc-slider-item\">\n                        <a href=\"https:\/\/openzeka.com\/en\/product\/nvidia-dgx-spark-triple\/\" target=\"_blank\" rel=\"nofollow noopener\">\n                            <div class=\"ext-wc-slider-content\">\n                                                                    <div class=\"ext-wc-slider-img\">\n                                        <img decoding=\"async\" src=\"https:\/\/openzeka.com\/en\/wp-content\/uploads\/2026\/06\/DGX_Spark_Triple_1.png\" alt=\"NVIDIA DGX Spark Triple\">\n                                    <\/div>\n                                \n                                <div class=\"ext-wc-slider-info\">\n                                                                            <div class=\"ext-wc-slider-category\">DGX Systems<\/div>\n                                    \n                                    <div class=\"ext-wc-slider-title\">NVIDIA DGX Spark Triple<\/div>\n\n                                    <div class=\"ext-wc-slider-btn\">\u0130ncele<\/div>\n                                <\/div>\n                            <\/div>\n                        <\/a>\n                    <\/div>\n                                    <div class=\"ext-wc-slider-item\">\n                        <a href=\"https:\/\/openzeka.com\/en\/product\/nvidia-dgx-spark-quad-ai-cluster-4-node-512-gb-200gbe\/\" target=\"_blank\" rel=\"nofollow noopener\">\n                            <div class=\"ext-wc-slider-content\">\n                                                                    <div class=\"ext-wc-slider-img\">\n                                        <img decoding=\"async\" src=\"https:\/\/openzeka.com\/en\/wp-content\/uploads\/2026\/07\/Dgx-spark-en-1.png\" alt=\"NVIDIA DGX Spark Quad AI Cluster \u2013 4 Node, 512 GB, 200GbE\">\n                                    <\/div>\n                                \n                                <div class=\"ext-wc-slider-info\">\n                                                                            <div class=\"ext-wc-slider-category\">DGX Systems<\/div>\n                                    \n                                    <div class=\"ext-wc-slider-title\">NVIDIA DGX Spark Quad AI Cluster \u2013 4 Node, 512 GB, 200GbE<\/div>\n\n                                    <div class=\"ext-wc-slider-btn\">\u0130ncele<\/div>\n                                <\/div>\n                            <\/div>\n                        <\/a>\n                    <\/div>\n                                    <div class=\"ext-wc-slider-item\">\n                        <a href=\"https:\/\/openzeka.com\/en\/product\/nvidia-dgx-spark-8-node-ai-cluster-8-node-1tb-200gbe\/\" target=\"_blank\" rel=\"nofollow noopener\">\n                            <div class=\"ext-wc-slider-content\">\n                                                                    <div class=\"ext-wc-slider-img\">\n                                        <img decoding=\"async\" src=\"https:\/\/openzeka.com\/en\/wp-content\/uploads\/2026\/08\/8spark.png\" alt=\"NVIDIA DGX Spark 8 Node AI Cluster \u2013 8 Node, 1TB, 200GbE\">\n                                    <\/div>\n                                \n                                <div class=\"ext-wc-slider-info\">\n                                                                            <div class=\"ext-wc-slider-category\">DGX Systems<\/div>\n                                    \n                                    <div class=\"ext-wc-slider-title\">NVIDIA DGX Spark 8 Node AI Cluster \u2013 8 Node, 1TB, 200GbE<\/div>\n\n                                    <div class=\"ext-wc-slider-btn\">\u0130ncele<\/div>\n                                <\/div>\n                            <\/div>\n                        <\/a>\n                    <\/div>\n                            <\/div>\n\n            <button class=\"ext-wc-arrow ext-wc-arrow-next\" aria-label=\"Sonraki\">&#10095;<\/button>\n        <\/div>\n    <\/div>\n\n    <style>\n        .ext-wc-slider-wrapper {\n            margin: 30px 0 !important;\n            width: 100% !important;\n            max-width: 100% !important;\n            box-sizing: border-box !important;\n            clear: both !important;\n            display: block !important;\n            font-family: inherit !important;\n        }\n\n        .ext-wc-slider-header {\n            display: flex !important;\n            justify-content: space-between !important;\n            align-items: center !important;\n            border-bottom: 2px solid #005885 !important;\n            padding-bottom: 10px !important;\n            margin-bottom: 14px !important;\n        }\n\n        .ext-wc-slider-heading {\n            font-size: 20px !important;\n            font-weight: 800 !important;\n            margin: 0 !important;\n            padding: 0 !important;\n            color: #1a1a1a !important; \n            text-transform: uppercase !important;\n            letter-spacing: 0.5px !important;\n            border: none !important;\n        }\n\n        .ext-wc-all-link {\n            font-size: 13px !important;\n            font-weight: 600 !important;\n            color: #005885 !important;\n            text-decoration: none !important;\n            display: inline-flex !important;\n            align-items: center !important;\n            gap: 6px !important;\n            transition: opacity .2s ease !important;\n        }\n\n        .ext-wc-all-link:hover {\n            opacity: 0.8 !important;\n            text-decoration: underline !important;\n        }\n\n        \/* OK BUTONLARI VE KAPSAYICI *\/\n        .ext-wc-slider-rel-container {\n            position: relative !important;\n            width: 100% !important;\n            padding: 0 16px !important;\n            box-sizing: border-box !important;\n        }\n\n        .ext-wc-arrow {\n            position: absolute !important;\n            top: 50% !important;\n            transform: translateY(-50%) !important;\n            width: 36px !important;\n            height: 36px !important;\n            background: #ffffff !important;\n            border: 1px solid #005885 !important;\n            color: #005885 !important;\n            border-radius: 50% !important;\n            display: flex !important;\n            align-items: center !important;\n            justify-content: center !important;\n            cursor: pointer !important;\n            z-index: 10 !important;\n            box-shadow: 0 2px 8px rgba(0,0,0,0.15) !important;\n            transition: all .2s ease !important;\n            font-size: 14px !important;\n            padding: 0 !important;\n            line-height: 1 !important;\n        }\n\n        \/* OKLARI KES\u0130N G\u0130ZLEME SINIFI *\/\n        .ext-wc-arrow.ext-wc-arrow-hidden {\n            display: none !important;\n        }\n\n        .ext-wc-arrow:hover {\n            background: #005885 !important;\n            color: #ffffff !important;\n        }\n\n        .ext-wc-arrow-prev {\n            left: -4px !important;\n        }\n\n        .ext-wc-arrow-next {\n            right: -4px !important;\n        }\n\n        \/* YATAY KAYDIRMA KUTUSU *\/\n        .ext-wc-slider-container {\n            display: flex !important;\n            flex-direction: row !important;\n            flex-wrap: nowrap !important;\n            justify-content: flex-start !important;\n            align-items: stretch !important;\n            gap: 16px !important;\n            overflow-x: auto !important;\n            overflow-y: hidden !important;\n            scroll-snap-type: x mandatory !important;\n            scroll-behavior: smooth !important;\n            -webkit-overflow-scrolling: touch !important;\n            padding: 6px 4px 12px 4px !important;\n            width: 100% !important;\n            box-sizing: border-box !important;\n            \n            scrollbar-width: none !important;\n            -ms-overflow-style: none !important;\n        }\n\n        .ext-wc-slider-container::-webkit-scrollbar {\n            display: none !important;\n        }\n\n        .ext-wc-slider-item {\n            flex: 0 0 360px !important;\n            min-width: 360px !important;\n            max-width: 360px !important;\n            scroll-snap-align: start !important;\n            border-radius: 6px !important;\n            border: 1px solid #e2e8f0 !important;\n            background: #ffffff !important;\n            box-shadow: 0 1px 3px rgba(0,0,0,0.05) !important;\n            transition: all .2s ease !important;\n            box-sizing: border-box !important;\n            margin: 0 !important;\n        }\n\n        .ext-wc-slider-item:hover {\n            box-shadow: 0 4px 12px rgba(0,0,0,0.1) !important;\n            border-color: #cbd5e1 !important;\n        }\n\n        .ext-wc-slider-item a {\n            text-decoration: none !important;\n            color: inherit !important;\n            display: block !important;\n            height: 100% !important;\n            padding: 14px !important;\n            box-sizing: border-box !important;\n        }\n\n        .ext-wc-slider-content {\n            display: flex !important;\n            flex-direction: row !important;\n            align-items: center !important;\n            gap: 14px !important;\n            height: 100% !important;\n        }\n\n        .ext-wc-slider-img {\n            width: 100px !important;\n            height: 90px !important;\n            flex-shrink: 0 !important;\n            display: flex !important;\n            align-items: center !important;\n            justify-content: center !important;\n            background: #f8fafc !important;\n            border-radius: 4px !important;\n            padding: 4px !important;\n        }\n\n        .ext-wc-slider-img img {\n            max-width: 100% !important;\n            max-height: 100% !important;\n            width: auto !important;\n            height: auto !important;\n            object-fit: contain !important;\n            display: block !important;\n        }\n\n        .ext-wc-slider-info {\n            display: flex !important;\n            flex-direction: column !important;\n            justify-content: center !important;\n            align-items: flex-start !important;\n            flex-grow: 1 !important;\n            min-width: 0 !important;\n        }\n\n        .ext-wc-slider-category {\n            font-size: 11px !important;\n            font-weight: 700 !important;\n            color: #005885 !important;\n            text-transform: uppercase !important;\n            letter-spacing: .6px !important;\n            margin-bottom: 4px !important;\n        }\n\n        .ext-wc-slider-title {\n            font-size: 13px !important;\n            font-weight: 700 !important;\n            color: #2d3748 !important;\n            line-height: 1.35 !important;\n            margin-bottom: 10px !important;\n            display: -webkit-box !important;\n            -webkit-line-clamp: 2 !important;\n            -webkit-box-orient: vertical !important;\n            overflow: hidden !important;\n            text-overflow: ellipsis !important;\n        }\n\n        .ext-wc-slider-btn {\n            display: inline-block !important;\n            padding: 4px 18px !important;\n            border: 1.5px solid #005885 !important;\n            color: #005885 !important;\n            background: transparent !important;\n            font-size: 12px !important;\n            font-weight: 600 !important;\n            border-radius: 2px !important;\n            transition: all .2s ease !important;\n        }\n\n        .ext-wc-slider-item:hover .ext-wc-slider-btn {\n            background: #005885 !important;\n            color: #ffffff !important;\n        }\n\n        @media (max-width: 768px) {\n            .ext-wc-slider-heading { font-size: 15px !important; }\n            .ext-wc-all-link { font-size: 11px !important; }\n            .ext-wc-slider-rel-container { padding: 0 10px !important; }\n            .ext-wc-slider-item {\n                flex: 0 0 280px !important;\n                min-width: 280px !important;\n                max-width: 280px !important;\n            }\n            .ext-wc-slider-img {\n                width: 80px !important;\n                height: 75px !important;\n            }\n            .ext-wc-slider-title {\n                font-size: 12px !important;\n                margin-bottom: 6px !important;\n            }\n            .ext-wc-slider-btn {\n                padding: 3px 12px !important;\n                font-size: 11px !important;\n            }\n            .ext-wc-arrow-prev { left: -6px !important; }\n            .ext-wc-arrow-next { right: -6px !important; }\n        }\n    <\/style>\n\n    <script>\n    document.addEventListener('DOMContentLoaded', function () {\n        const wrappers = document.querySelectorAll('.ext-wc-slider-rel-container');\n\n        wrappers.forEach(wrapper => {\n            const container = wrapper.querySelector('.ext-wc-slider-container');\n            const prevBtn = wrapper.querySelector('.ext-wc-arrow-prev');\n            const nextBtn = wrapper.querySelector('.ext-wc-arrow-next');\n\n            if (!container || !prevBtn || !nextBtn) return;\n\n            const itemsCount = container.querySelectorAll('.ext-wc-slider-item').length;\n\n            \/\/ Ekran geni\u015fli\u011fine g\u00f6re \u00fcr\u00fcn say\u0131s\u0131na bak: Masa\u00fcst\u00fcnde >= 3, Mobilde >= 2 ise g\u00f6ster\n            function checkArrowVisibility() {\n                const isMobile = window.innerWidth <= 768;\n                const minRequired = isMobile ? 2 : 3;\n\n                if (itemsCount < minRequired) {\n                    prevBtn.classList.add('ext-wc-arrow-hidden');\n                    nextBtn.classList.add('ext-wc-arrow-hidden');\n                } else {\n                    prevBtn.classList.remove('ext-wc-arrow-hidden');\n                    nextBtn.classList.remove('ext-wc-arrow-hidden');\n                }\n            }\n\n            checkArrowVisibility();\n            window.addEventListener('resize', checkArrowVisibility);\n\n            function getScrollAmount() {\n                return window.innerWidth <= 768 ? 296 : 376;\n            }\n\n            prevBtn.addEventListener('click', function () {\n                container.scrollBy({ left: -getScrollAmount(), behavior: 'smooth' });\n            });\n\n            nextBtn.addEventListener('click', function () {\n                container.scrollBy({ left: getScrollAmount(), behavior: 'smooth' });\n            });\n        });\n    });\n    <\/script>\n\n    <div class=\"fusion-text fusion-text-3\"><p>The software and knowledge ecosystem surrounding the device is extensive. Models, Docker images, NVIDIA playbooks, and forums are only a few of its components. DGX Spark has also developed a broad developer community. Community-built images, multi-device recipes, tools, software improvements, and performance rankings have expanded the ecosystem well beyond NVIDIA&#8217;s official resources.<\/p>\n<\/div><div class=\"fusion-text fusion-text-4\"><h2>Models<\/h2>\n<\/div><div class=\"fusion-text fusion-text-5\"><p>Models intended to run on DGX Spark are hosted on <a style=\"color: #18cc48;\" href=\"https:\/\/huggingface.co\/\"><strong>Hugging Face<\/strong><\/a>, and many are available in multiple quantization formats. A single model may have FP8, NVFP4, MXFP4, or INT4 variants on Hugging Face. DGX Spark provides hardware support for all of these formats, allowing compatible models to be run after download without requiring an additional conversion step.<\/p>\n<\/div><div class=\"fusion-text fusion-text-6\"><p>Among these quantization formats, NVFP4 is an FP4 format specifically designed for the Blackwell architecture used by DGX Spark. By representing model weights at a lower bit width, it substantially reduces memory requirements. For example, a 200-billion-parameter model that would require approximately 400 GB of memory in a standard 16-bit format can be reduced to roughly one-quarter of that size with NVFP4. This makes it possible to run significantly larger models within the system&#8217;s 128 GB of unified memory.<\/p>\n<\/div><div class=\"fusion-text fusion-text-7\"><p>Hugging Face hosts a <a style=\"color: #18cc48;\" href=\"https:\/\/huggingface.co\/nvidia\"><strong>collection of nearly 100 NVFP4 models converted by NVIDIA<\/strong><\/a>. The community has also published a wide range of models specifically prepared for Spark.<\/p>\n<\/div><div class=\"fusion-text fusion-text-8\"><h2>Inference Engines<\/h2>\n<\/div><div class=\"fusion-text fusion-text-9\"><p>Inference engines are software systems that load model weights into GPU memory and expose APIs for accepting external requests. These APIs can then be used by Open WebUI, Odysseus, or any other compatible client. Common inference engines include <a style=\"color: #18cc48;\" href=\"https:\/\/docs.vllm.ai\/\"><strong>vLLM<\/strong><\/a>, <a style=\"color: #18cc48;\" href=\"https:\/\/docs.sglang.ai\/\"><strong>SGLang<\/strong><\/a>, <a style=\"color: #18cc48;\" href=\"https:\/\/nvidia.github.io\/TensorRT-LLM\/\"><strong>TensorRT-LLM<\/strong><\/a>, and <a style=\"color: #18cc48;\" href=\"https:\/\/github.com\/ggml-org\/llama.cpp\"><strong>llama.cpp<\/strong><\/a>.<\/p>\n<\/div><div class=\"fusion-text fusion-text-10\"><p>The most widely used inference engine on DGX Spark is vLLM. One of the main reasons is its strong support for distributed execution. In particular, its robust multi-node tensor parallelism capabilities make it well suited to multi-Spark deployments. Other important features include PagedAttention for efficient GPU memory utilization, prefix caching for reusing computations across repeated prompts, and support for lower-precision KV cache formats such as FP8 and NVFP4.<\/p>\n<\/div><div class=\"fusion-text fusion-text-11\"><p>vLLM is commonly run inside Docker containers. Docker images are used to package the runtime together with its GPU dependencies. Official vLLM images are published on Docker Hub under <a style=\"color: #18cc48;\" href=\"https:\/\/hub.docker.com\/r\/vllm\/vllm-openai\"><strong><code>vllm\/vllm-openai<\/code><\/strong><\/a>.<\/p>\n<\/div><div class=\"fusion-text fusion-text-12\"><h2>Community<\/h2>\n<\/div><div class=\"fusion-text fusion-text-13\"><p>The GB10 chip used in DGX Spark has an SM 12.1 compute capability, which differs from that of conventional NVIDIA GPUs. Official Docker images for inference engines such as vLLM may not include native kernels for this architecture. In other words, models may still run with official vLLM images, but some workloads may not fully utilize the performance capabilities of the Spark GPU. For this reason, specialized images and deployment recipes are sometimes required.<\/p>\n<\/div><div class=\"fusion-text fusion-text-14\"><h3>Developers<\/h3>\n<\/div><div class=\"fusion-text fusion-text-15\"><p>Community developers build and publish Docker images containing native SM 12.1a kernels for GB10. As a result, community-maintained images are frequently used alongside official vLLM images when working with DGX Spark.<\/p>\n<\/div><div class=\"fusion-text fusion-text-16\"><p>Some of the most prominent developers and their contributions to the Spark ecosystem are summarized below. New contributors continue to appear regularly:<\/p>\n<\/div><div class=\"fusion-text fusion-text-17\"><ul>\n<li><strong>eugr<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/github.com\/eugr\/spark-vllm-docker\"><strong>spark-vllm-docker<\/strong><\/a> \u2014 General-purpose inference images, MXFP4 builds, and the B12X kernel family<\/li>\n<li><strong>christopherowen<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/github.com\/christopherowen\/vllm\"><strong>vllm (fork)<\/strong><\/a> \u2014 Source code for CUTLASS and FlashInfer kernels<\/li>\n<li><strong>local-inference-lab<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/github.com\/local-inference-lab\/vllm\"><strong>vllm (fork)<\/strong><\/a> \u2014 Source of the B12X kernel family and experimental high-performance kernels for the sm12x architecture<\/li>\n<li><strong>tonyd2wild<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/github.com\/tonyd2wild\"><strong>tonyd2wild<\/strong><\/a> \u2014 Model-specific images, multi-Spark deployment recipes for DeepSeek V4 Flash, GLM-5.2, MiMo V2.5, and MiniMax-M3, as well as DSpark speculative decoding patches<\/li>\n<li><strong>mpfaffenberger<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/github.com\/mpfaffenberger\"><strong>mpfaffenberger<\/strong><\/a> \u2014 Runtime modifications addressing model compatibility issues<\/li>\n<li><strong>MiaAI-Lab<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/github.com\/MiaAI-Lab\"><strong>MiaAI-Lab<\/strong><\/a> \u2014 Community deployment recipes for DeepSeek V4 Flash, GLM-5.2, and Laguna S, as well as the sparkDash monitoring tool<\/li>\n<\/ul>\n<\/div><div class=\"fusion-text fusion-text-18\"><p>In addition, <a style=\"color: #18cc48;\" href=\"https:\/\/spark-arena.com\/\"><strong>Spark Arena<\/strong><\/a> maintains an <a style=\"color: #18cc48;\" href=\"https:\/\/spark-arena.com\/leaderboard\"><strong>LLM performance leaderboard<\/strong><\/a> ranking the performance of different models and inference engines on DGX Spark.<\/p>\n<\/div><div class=\"fusion-text fusion-text-19\"><h3>Tools<\/h3>\n<\/div><div class=\"fusion-text fusion-text-20\"><p>Docker alone is sufficient for running models on a single DGX Spark. With multiple Sparks, however, manually managing Docker containers, creating the cluster, and configuring the CX-7 network, SSH mesh, and NCCL communication can become considerably more complex. The community has developed several tools to simplify this process.<\/p>\n<\/div><div class=\"fusion-text fusion-text-21\"><p><a style=\"color: #18cc48;\" href=\"https:\/\/github.com\/eugr\/spark-vllm-docker\"><strong>eugr\/spark-vllm-docker<\/strong><\/a>, developed by eugr, is one of the most comprehensive toolsets for building Docker images with native SM 12.1a kernels for GB10. It includes build scripts, cluster initialization, recipe deployment, model downloading, node discovery, runtime modifications, and ready-to-use recipes. These tools are widely used by the community.<\/p>\n<\/div><div class=\"fusion-text fusion-text-22\"><p><a style=\"color: #18cc48;\" href=\"https:\/\/sparkrun.dev\/\"><strong>sparkrun<\/strong><\/a>, developed by Spark Arena, automates cluster management. Its setup wizard configures the CX-7 network, SSH mesh, and NCCL environment with a single command. A YAML recipe file can then be used to define the model, Docker image, inference parameters, and memory configuration. sparkrun also provides comprehensive real-time cluster monitoring and has become a widely used cluster-management tool within the DGX Spark ecosystem.<\/p>\n<\/div><div class=\"fusion-text fusion-text-23\"><h2>OpenZeka Resources<\/h2>\n<\/div><div class=\"fusion-text fusion-text-24\"><p>At OpenZeka, we publish Turkish and English tutorials covering our work with DGX Spark. English-language articles are available at <a style=\"color: #18cc48;\" href=\"https:\/\/blog.openzeka.com\/en\/\"><strong>blog.openzeka.com\/en\/<\/strong><\/a>. The blog primarily focuses on user education, including tutorials, step-by-step model and tool installation instructions, and practical usage guides. Some of our DGX Spark articles are listed below:<\/p>\n<\/div><div class=\"fusion-text fusion-text-25\"><ul>\n<li><strong><a style=\"color: #18cc48;\" href=\"https:\/\/blog.openzeka.com\/en\/serving-local-llm-on-dgx-spark\/\"><strong>Serving a Local LLM with vLLM on DGX Spark<\/strong><\/a><\/strong>: Single Spark, vLLM, Open WebUI<\/li>\n<li><strong><a style=\"color: #18cc48;\" href=\"https:\/\/blog.openzeka.com\/en\/local-gpt-oss-120b-serving-on-dgx-spark-with-sparkrun\/\"><strong>Local GPT-OSS 120B Serving on DGX Spark with sparkrun<\/strong><\/a><\/strong>: Single\/dual Spark, GPT-OSS 120B, vLLM, sparkrun<\/li>\n<li><strong><a style=\"color: #18cc48;\" href=\"https:\/\/blog.openzeka.com\/en\/deepseek-v4-flash-0731-on-2x-dgx-spark\/\"><strong>DeepSeek-V4-Flash-0731 on 2x DGX Spark<\/strong><\/a><\/strong>: 2\u00d7 Spark, DeepSeek-V4-Flash-0731, vLLM, sparkrun<\/li>\n<li><strong><a style=\"color: #18cc48;\" href=\"https:\/\/blog.openzeka.com\/en\/local-ai-workspace-with-odysseus-on-dgx-spark\/\"><strong>Local AI Workspace with Odysseus on DGX Spark<\/strong><\/a><\/strong>: Local AI workspace, agents, RAG, tool use<\/li>\n<li><strong><a style=\"color: #18cc48;\" href=\"https:\/\/blog.openzeka.com\/en\/local-model-fine-tuning-with-unsloth-on-dgx-spark\/\"><strong>Local Model Fine-Tuning with Unsloth on DGX Spark<\/strong><\/a><\/strong>: Fine-tuning, Unsloth, QLoRA<\/li>\n<li><strong><a style=\"color: #18cc48;\" href=\"https:\/\/blog.openzeka.com\/en\/gpu-accelerated-data-science-and-machine-learning-on-dgx-spark\/\"><strong>GPU-Accelerated Data Science and Machine Learning on DGX Spark<\/strong><\/a><\/strong>: CUDA-X, RAPIDS, cuDF, cuML<\/li>\n<li><strong><a style=\"color: #18cc48;\" href=\"https:\/\/blog.openzeka.com\/en\/building-a-high-speed-ai-cluster-with-4-dgx-spark-systems\/\"><strong>Building a High-Speed AI Cluster with 4 DGX Spark Systems<\/strong><\/a><\/strong>: 4\u00d7 Spark cluster, 200GbE, RDMA, RoCEv2<\/li>\n<\/ul>\n<\/div><div class=\"fusion-text fusion-text-26\"><p>Our white papers are published at <a style=\"color: #18cc48;\" href=\"https:\/\/whitepapers.openzeka.com\/\"><strong>whitepapers.openzeka.com<\/strong><\/a>. Unlike the blog&#8217;s user-education-oriented content, the white papers target a more technical and professional audience. They provide in-depth analyses intended to support decision-making in areas such as hardware selection, benchmark interpretation, cluster architecture comparison, and scaling strategies. Some of our white papers are listed below:<\/p>\n<\/div><div class=\"fusion-text fusion-text-27\"><ul>\n<li><strong><a style=\"color: #18cc48;\" href=\"https:\/\/whitepapers.openzeka.com\/papers\/yerel-llm-rehberi\/\"><strong>Local LLM Usage Guide<\/strong><\/a><\/strong>: Hardware, model, and software selection guide<\/li>\n<li><strong><a style=\"color: #18cc48;\" href=\"https:\/\/whitepapers.openzeka.com\/papers\/qwen3.6-27b-dgx-spark-benchmark\/\"><strong>Qwen3.6-27B DGX Spark Benchmark<\/strong><\/a><\/strong>: FP8, AWQ, and NVFP4 performance analysis<\/li>\n<li><strong><a style=\"color: #18cc48;\" href=\"https:\/\/whitepapers.openzeka.com\/papers\/qwen3.6-27b-dgx-spark-scaling\/\"><strong>Qwen3.6-27B DGX Spark Cluster Scaling<\/strong><\/a><\/strong>: 1\u00d7\/2\u00d7\/4\u00d7 Spark scaling analysis<\/li>\n<li><strong><a style=\"color: #18cc48;\" href=\"https:\/\/whitepapers.openzeka.com\/papers\/dgx-spark-2node-cluster-kurulumu\/\"><strong>DGX Spark 2-Node Cluster Setup Guide<\/strong><\/a><\/strong>: Two-node cluster setup<\/li>\n<li><strong><a style=\"color: #18cc48;\" href=\"https:\/\/whitepapers.openzeka.com\/papers\/dgx-spark-3node-cluster-kurulumu\/\"><strong>DGX Spark 3-Node Cluster Setup Guide<\/strong><\/a><\/strong>: Three-node ring topology<\/li>\n<li><strong><a style=\"color: #18cc48;\" href=\"https:\/\/whitepapers.openzeka.com\/papers\/dgx-spark-4node-cluster-kurulumu\/\"><strong>DGX Spark 4-Node Cluster Setup Guide<\/strong><\/a><\/strong>: Four-node switch topology<\/li>\n<\/ul>\n<\/div><div class=\"fusion-text fusion-text-28\"><p>We also publish a variety of tutorials, tests, and product-analysis videos on the <a style=\"color: #18cc48;\" href=\"https:\/\/www.youtube.com\/@openzeka\"><strong>OpenZeka<\/strong><\/a> and <a style=\"color: #18cc48;\" href=\"https:\/\/www.youtube.com\/@cordatusai\"><strong>CordatusAI<\/strong><\/a> YouTube channels.<\/p>\n<\/div><div class=\"fusion-text fusion-text-29\"><h2>Forums and Documentation<\/h2>\n<\/div><div class=\"fusion-text fusion-text-30\"><p>The <a style=\"color: #18cc48;\" href=\"https:\/\/forums.developer.nvidia.com\/c\/accelerated-computing\/dgx-spark-gb10\/719\"><strong>DGX Spark \/ GB10<\/strong><\/a> category of the NVIDIA Developer Forums is a community discussion space in which NVIDIA engineers also actively participate. It contains hundreds of discussions covering model deployment recipes, performance optimization, newly released models, and many other topics. It is one of the first resources worth consulting when encountering a DGX Spark-related issue. The <a style=\"color: #18cc48;\" href=\"https:\/\/developer.nvidia.com\/topics\/ai\/dgx-spark\"><strong>NVIDIA Developer<\/strong><\/a> page brings together links to this forum, official documentation, and playbooks in one place.<\/p>\n<\/div><div class=\"fusion-text fusion-text-31\"><p>Official DGX Spark documentation is available at <a style=\"color: #18cc48;\" href=\"https:\/\/docs.nvidia.com\/dgx\/dgx-spark\/index.html\"><strong>docs.nvidia.com\/dgx\/dgx-spark<\/strong><\/a>. The <a style=\"color: #18cc48;\" href=\"https:\/\/docs.nvidia.com\/dgx\/dgx-spark\/index.html\"><strong>User Guide<\/strong><\/a> covers hardware specifications, DGX OS, ConnectX-7 clustering, system recovery, and known issues, with dedicated pages for <a style=\"color: #18cc48;\" href=\"https:\/\/docs.nvidia.com\/dgx\/dgx-spark\/hardware.html\"><strong>hardware specifications<\/strong><\/a>, <a style=\"color: #18cc48;\" href=\"https:\/\/docs.nvidia.com\/dgx\/dgx-spark\/os-and-component-update.html\"><strong>OS and component updates<\/strong><\/a>, and <a style=\"color: #18cc48;\" href=\"https:\/\/docs.nvidia.com\/dgx\/dgx-spark\/release-notes.html\"><strong>release notes<\/strong><\/a>. For multi-Spark deployments, NVIDIA provides documentation for the <a style=\"color: #18cc48;\" href=\"https:\/\/docs.nvidia.com\/sync\/latest\/cluster-assistant.html\"><strong>NVIDIA Sync Cluster Assistant<\/strong><\/a> and <a style=\"color: #18cc48;\" href=\"https:\/\/docs.nvidia.com\/sync\/latest\/cluster-network-inspection.html\"><strong>cluster network inspection<\/strong><\/a>. For performance analysis, documentation is available for both <a style=\"color: #18cc48;\" href=\"https:\/\/docs.nvidia.com\/nsight-systems\/UserGuide\/index.html\"><strong>Nsight Systems<\/strong><\/a> and <a style=\"color: #18cc48;\" href=\"https:\/\/docs.nvidia.com\/nsight-compute\/ProfilingGuide\/index.html\"><strong>Nsight Compute<\/strong><\/a>.<\/p>\n<\/div><div class=\"fusion-text fusion-text-32\"><h2>Spark Playbook Catalog<\/h2>\n<\/div><div class=\"fusion-text fusion-text-33\"><p>NVIDIA provides a collection of <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\"><strong>Spark Playbooks<\/strong><\/a> for DGX Spark. Each playbook contains step-by-step installation and execution instructions for a particular tool or workflow. Some of these tools directly run models, while others provide the surrounding infrastructure required to make those models usable in practical applications.<\/p>\n<\/div><div class=\"fusion-text fusion-text-34\"><h3>Inference Engines<\/h3>\n<\/div><div class=\"fusion-text fusion-text-35\"><p>Models are typically developed using machine learning frameworks such as PyTorch or JAX. Inference engines load or convert the resulting model weights and optimize GPU kernels, KV cache management, batching, and memory utilization to execute the model efficiently. Some also provide an OpenAI-compatible API through either a built-in or separate model-server component.<\/p>\n<\/div><div class=\"fusion-text fusion-text-36\"><ul>\n<li><strong>TensorRT-LLM<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/trt-llm\"><strong>trt-llm<\/strong><\/a> \u2014 TensorRT-LLM is an open-source inference library optimized for LLM execution on NVIDIA GPUs. It uses optimized kernels and supports tensor, pipeline, and sequence parallelism. It integrates with Hugging Face and PyTorch.<\/li>\n<li><strong>vLLM<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/vllm\"><strong>vllm<\/strong><\/a> \u2014 vLLM is a high-throughput inference engine that uses PagedAttention and continuous batching to improve memory efficiency. It also provides an OpenAI-compatible model server.<\/li>\n<li><strong>llama.cpp<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/llama-cpp\"><strong>llama-cpp<\/strong><\/a> \u2014 llama.cpp is a lightweight C\/C++ inference engine for running GGUF models across CPUs and multiple GPU backends. Its <code>llama-server<\/code> component can expose an OpenAI-compatible API.<\/li>\n<li><strong>SGLang<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/sglang\"><strong>sglang<\/strong><\/a> \u2014 SGLang is a high-performance inference and model-serving framework for LLMs and VLMs. It jointly optimizes the backend runtime and model interaction layer.<\/li>\n<li><strong>NVIDIA NIM<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/nim-llm\"><strong>nim-llm<\/strong><\/a> \u2014 NIM is NVIDIA&#8217;s solution for packaging inference engines such as those listed above into containerized HTTP microservices. Rather than being an inference engine itself, it acts as a packaging and deployment layer that prepares these engines for production and enterprise use.<\/li>\n<\/ul>\n<\/div><div class=\"fusion-text fusion-text-37\"><h3>Applications and Tools<\/h3>\n<\/div><div class=\"fusion-text fusion-text-38\"><p>These tools allow users and developers to interact directly with models. Live VLM WebUI and Continue connect to an existing backend service, whereas Ollama and LM Studio also include their own inference runtimes.<\/p>\n<\/div><div class=\"fusion-text fusion-text-39\"><ul>\n<li><strong>Ollama + Open WebUI<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/open-webui\"><strong>open-webui<\/strong><\/a> \u2014 Ollama is a local model-serving layer built on llama.cpp that provides a CLI, model library, and OpenAI-compatible API. Open WebUI is a self-hosted web interface that connects to this service.<\/li>\n<li><strong>LM Studio<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/lm-studio\"><strong>lm-studio<\/strong><\/a> \u2014 LM Studio is a local AI application that combines model discovery, execution, and API-based serving.<\/li>\n<li><strong>Live VLM WebUI<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/live-vlm-webui\"><strong>live-vlm-webui<\/strong><\/a> \u2014 Live VLM WebUI is a web interface that sends webcam streams to Ollama, vLLM, SGLang, or cloud-hosted VLM services. It allows the outputs and performance of different backend models to be compared.<\/li>\n<li><strong>Continue + VS Code<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/vibe-coding\"><strong>vibe-coding<\/strong><\/a> \u2014 Continue is a coding client that runs inside VS Code. In the playbook, Ollama serves the model while Continue connects to that service for chat and code generation.<\/li>\n<\/ul>\n<\/div><div class=\"fusion-text fusion-text-40\"><h3>Models and Inference Optimizations<\/h3>\n<\/div><div class=\"fusion-text fusion-text-41\"><p>Nemotron is NVIDIA&#8217;s model family. Speculative decoding and NVFP4, meanwhile, are techniques intended to improve the performance or memory efficiency of compatible inference engines.<\/p>\n<\/div><div class=\"fusion-text fusion-text-42\"><ul>\n<li><strong>NVIDIA Nemotron<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/nemotron\"><strong>nemotron<\/strong><\/a> \u2014 Nemotron is NVIDIA&#8217;s open model family designed for reasoning, tool use, and long-context tasks. It can be served with llama.cpp, vLLM, SGLang, or TensorRT-LLM.<\/li>\n<li><strong>Speculative decoding<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/speculative-decoding\"><strong>speculative-decoding<\/strong><\/a> \u2014 Speculative decoding accelerates the decoding process by having a smaller, faster draft model propose tokens that are then verified in batches by the target model. The playbook demonstrates this technique with EAGLE-3 and Draft\u2013Target methods in TensorRT-LLM.<\/li>\n<li><strong>NVFP4 quantization<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/nvfp4-quantization\"><strong>nvfp4-quantization<\/strong><\/a> \u2014 NVFP4 is a Blackwell-compatible 4-bit quantization format for model weights and activations. The playbook converts the model with TensorRT Model Optimizer and then runs it in a TensorRT-LLM environment.<\/li>\n<\/ul>\n<\/div><div class=\"fusion-text fusion-text-43\"><h3>Fine-Tuning Frameworks and Tools<\/h3>\n<\/div><div class=\"fusion-text fusion-text-44\"><p>Fine-tuning tools use existing model weights to produce updated model weights. The resulting model can subsequently be run using an inference engine.<\/p>\n<\/div><div class=\"fusion-text fusion-text-45\"><p>PyTorch is the underlying machine learning framework. NeMo, LLaMA-Factory, and Unsloth provide more streamlined and optimized training workflows built on top of PyTorch.<\/p>\n<\/div><div class=\"fusion-text fusion-text-46\"><ul>\n<li><strong>NVIDIA NeMo<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/nemo-fine-tune\"><strong>nemo-fine-tune<\/strong><\/a> \u2014 NeMo AutoModel is a framework for training Hugging Face models with native PyTorch support and scaling them to multi-node systems. It supports SFT, PEFT, and distributed training workflows.<\/li>\n<li><strong>LLaMA-Factory<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/llama-factory\"><strong>llama-factory<\/strong><\/a> \u2014 LLaMA-Factory is a fine-tuning framework that provides a common interface for optimization methods such as SFT, LoRA, and QLoRA across different LLMs and VLMs.<\/li>\n<li><strong>Unsloth<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/unsloth\"><strong>unsloth<\/strong><\/a> \u2014 Unsloth is a fine-tuning toolkit that uses custom GPU kernels to accelerate LoRA and QLoRA training while reducing memory consumption.<\/li>\n<li><strong>PyTorch<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/pytorch-fine-tune\"><strong>pytorch-fine-tune<\/strong><\/a> \u2014 PyTorch is a foundational machine learning framework that provides tensor operations, automatic differentiation, and GPU acceleration. This playbook implements SFT, LoRA, and QLoRA workflows directly in PyTorch at a lower level of abstraction.<\/li>\n<li><strong>FLUX.1 DreamBooth LoRA<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/flux-finetuning\"><strong>flux-finetuning<\/strong><\/a> \u2014 This playbook uses DreamBooth LoRA to teach new concepts and styles to the FLUX.1-dev diffusion image-generation model.<\/li>\n<\/ul>\n<\/div><div class=\"fusion-text fusion-text-47\"><h3>Agentic AI Systems<\/h3>\n<\/div><div class=\"fusion-text fusion-text-48\"><p>AI agents connect to an LLM service for decision-making while interacting with files, terminal commands, APIs, and other tools. The model itself is run by a service such as vLLM or Ollama, while the agent application manages task planning, memory, and tool use.<\/p>\n<\/div><div class=\"fusion-text fusion-text-49\"><p>OpenClaw and Hermes are agent applications. OpenShell is a sandbox runtime that surrounds the agent process with filesystem, network, permission, and security policies. NemoClaw combines these layers into an integrated deployment.<\/p>\n<\/div><div class=\"fusion-text fusion-text-50\"><ul>\n<li><strong>OpenClaw<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/openclaw\"><strong>openclaw<\/strong><\/a> \u2014 OpenClaw is a persistent local agent application combining memory, filesystem access, tool use, and skills. In the playbook, its decision-making model is served through vLLM.<\/li>\n<li><strong>Hermes Agent<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/hermes-agent\"><strong>hermes-agent<\/strong><\/a> \u2014 Hermes is an autonomous agent capable of generating reusable skills from its experiences, retaining memory across sessions, and running scheduled tasks. In the playbook, vLLM is used as the local model service.<\/li>\n<li><strong>OpenShell<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/openshell\"><strong>openshell<\/strong><\/a> \u2014 OpenShell is a sandbox runtime that constrains agents at the kernel level. The playbook runs OpenClaw inside the sandbox while the model service is accessed through an endpoint outside the sandbox.<\/li>\n<li><strong>NemoClaw<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/nemoclaw\"><strong>nemoclaw<\/strong><\/a> \u2014 NemoClaw combines the OpenClaw agent, OpenShell sandbox, and local vLLM inference. Filesystem, network, process, and inference access are configured within a unified deployment.<\/li>\n<li><strong>NemoClaw example agents<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/nemoclaw-applications\"><strong>nemoclaw-applications<\/strong><\/a> \u2014 This playbook deploys news summarization, software development, document review, and calendar negotiation agents on top of an existing NemoClaw sandbox, demonstrating how the same infrastructure can be adapted to different applications.<\/li>\n<li><strong>CLI Coding Agents<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/cli-coding-agent\"><strong>cli-coding-agent<\/strong><\/a> \u2014 Claude Code, OpenCode, and Codex CLI are terminal-based agents that manage file editing, command execution, and coding tasks. In the playbook, model inference is provided by Ollama.<\/li>\n<\/ul>\n<\/div><div class=\"fusion-text fusion-text-51\"><h3>Complete AI Applications and Solutions<\/h3>\n<\/div><div class=\"fusion-text fusion-text-52\"><p>These applications combine models, inference engines, data processing, user interfaces, and agent components to solve specific problems. Instead of interacting with individual infrastructure layers, users interact with a complete end-to-end system.<\/p>\n<\/div><div class=\"fusion-text fusion-text-53\"><ul>\n<li><strong>RAG Application in AI Workbench<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/rag-ai-workbench\"><strong>rag-ai-workbench<\/strong><\/a> \u2014 An agentic RAG application combining query routing, information retrieval, answer generation, and hallucination evaluation. It can use either NVIDIA-hosted APIs or self-hosted model services.<\/li>\n<li><strong>Video Search and Summarization<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/vss\"><strong>vss<\/strong><\/a> \u2014 VSS uses VLM, LLM, and RAG components to generate video summaries, question-answering capabilities, and real-time alerts.<\/li>\n<li><strong>Multi-Agent Chatbot<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/multi-agent-chatbot\"><strong>multi-agent-chatbot<\/strong><\/a> \u2014 A supervisor agent coordinates coding, RAG, and visual-understanding agents. llama.cpp and TensorRT-LLM servers are used for model serving, while MCP is used for tool connectivity.<\/li>\n<li><strong>Image Generation with TensorRT<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/multi-modal-inference\"><strong>multi-modal-inference<\/strong><\/a> \u2014 This playbook optimizes the FLUX.1 and SDXL diffusion models with TensorRT for text-to-image generation.<\/li>\n<li><strong>Text to Knowledge Graph<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/txt2kg\"><strong>txt2kg<\/strong><\/a> \u2014 Extracts subject\u2013predicate\u2013object relationships from text using Ollama, stores the resulting data in ArangoDB, and visualizes it with Three.js WebGPU. Ollama performs inference while ArangoDB handles relationship queries.<\/li>\n<li><strong>Spark &amp; Reachy Photo Booth<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/spark-reachy-photo-booth\"><strong>spark-reachy-photo-booth<\/strong><\/a> \u2014 A multimodal application combining NeMo Agent Toolkit, TensorRT-LLM, speech recognition, speech synthesis, FLUX image generation, and object tracking with the Reachy Mini robot. Services communicate through a message bus.<\/li>\n<\/ul>\n<\/div><div class=\"fusion-text fusion-text-54\"><h3>Other GPU-Accelerated Workloads<\/h3>\n<\/div><div class=\"fusion-text fusion-text-55\"><p>These playbooks are not part of AI inference or model-serving workflows. Instead, they demonstrate how DGX Spark&#8217;s computational acceleration capabilities can be applied to areas such as data science, optimization, bioinformatics, and robotics.<\/p>\n<\/div><div class=\"fusion-text fusion-text-56\"><ul>\n<li><strong>CUDA-X Data Science<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/cuda-x-data-science\"><strong>cuda-x-data-science<\/strong><\/a> \u2014 CUDA-X Data Science uses RAPIDS libraries such as cuDF and cuML to accelerate pandas and scikit-learn workflows on the GPU.<\/li>\n<li><strong>Single-cell RNA Sequencing<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/single-cell\"><strong>single-cell<\/strong><\/a> \u2014 RAPIDS-singlecell provides a Scanpy-like API for running scRNA-seq preprocessing, quality control, clustering, and visualization on the GPU. It uses RAPIDS components such as cuML and cuGraph.<\/li>\n<li><strong>Portfolio Optimization<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/portfolio-optimization\"><strong>portfolio-optimization<\/strong><\/a> \u2014 cuOpt solves portfolio constraints as LP\/MILP optimization problems, while cuML accelerates the generation of risk scenarios. The playbook combines these tools in an end-to-end Mean-CVaR-based financial workflow.<\/li>\n<li><strong>Isaac Sim and Isaac Lab<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/isaac\"><strong>isaac<\/strong><\/a> \u2014 Isaac Sim is a GPU-accelerated robotics simulation platform. Isaac Lab is a framework built on top of this environment for developing reinforcement-learning policies.<\/li>\n<\/ul>\n<\/div><div class=\"fusion-text fusion-text-57\"><h3>Multi-DGX Spark Deployments<\/h3>\n<\/div><div class=\"fusion-text fusion-text-58\"><p>These playbooks prepare multi-device infrastructure independently of the model or application layer. First, the QSFP network and inter-node SSH access are configured. NCCL then provides high-performance collective GPU communication over this connection for distributed training and inference.<\/p>\n<\/div><div class=\"fusion-text fusion-text-59\"><p>Higher-level software such as vLLM, TensorRT-LLM, PyTorch, and NeMo can use this infrastructure when operating in distributed configurations.<\/p>\n<\/div><div class=\"fusion-text fusion-text-60\"><ul>\n<li><strong>2\u00d7 DGX Spark direct connection<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/connect-two-sparks\"><strong>connect-two-sparks<\/strong><\/a> \u2014 Connects two DGX Spark systems directly through a 200 GbE QSFP link and passwordless SSH, creating a two-node distributed system.<\/li>\n<li><strong>3\u00d7 DGX Spark ring connection<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/connect-three-sparks\"><strong>connect-three-sparks<\/strong><\/a> \u2014 Connects three DGX Spark systems in a ring topology using three QSFP cables.<\/li>\n<li><strong>4+ DGX Spark switch connection<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/multi-sparks-through-switch\"><strong>multi-sparks-through-switch<\/strong><\/a> \u2014 Connects four or more DGX Spark systems through a QSFP switch to create a scalable cluster.<\/li>\n<li><strong>NCCL<\/strong>: <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\/nccl\"><strong>nccl<\/strong><\/a> \u2014 NCCL is NVIDIA&#8217;s library for high-performance collective communication between GPUs located on different nodes.<\/li>\n<\/ul>\n<\/div><div class=\"fusion-text fusion-text-61\"><p>All of these playbooks are available at <a style=\"color: #18cc48;\" href=\"https:\/\/build.nvidia.com\/spark\"><strong>build.nvidia.com\/spark<\/strong><\/a>.<\/p>\n<\/div><div class=\"fusion-text fusion-text-62\"><h2>Conclusion<\/h2>\n<\/div><div class=\"fusion-text fusion-text-63\"><p>DGX Spark has a strong and rapidly evolving ecosystem built on NVIDIA&#8217;s foundations and continuously expanded through a growing range of community projects. OpenZeka contributes to this ecosystem by making its technical knowledge more accessible to end users through Turkish- and English-language tutorials, technical analysis documents, practical guides, and YouTube videos.<\/p>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":3,"featured_media":1976,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[17],"tags":[],"class_list":["post-1973","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-generative-ai"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.3.1 (Yoast SEO v28.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>NVIDIA DGX Spark Ecosystem - OpenZeka EN Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"NVIDIA DGX Spark Ecosystem\" \/>\n<meta property=\"og:url\" content=\"https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/\" \/>\n<meta property=\"og:site_name\" content=\"OpenZeka EN Blog\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/profile.php?id=61576911356211\" \/>\n<meta property=\"article:published_time\" content=\"2026-08-24T12:34:27+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-08-24T12:35:19+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/blog.openzeka.com\/en\/wp-content\/uploads\/2026\/08\/ekosistem-blog-resim.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"1080\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Enhar\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@Aetherixnl\" \/>\n<meta name=\"twitter:site\" content=\"@Aetherixnl\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Enhar\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"14 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/nvidia-dgx-spark-ecosystem\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/nvidia-dgx-spark-ecosystem\\\/\"},\"author\":{\"name\":\"Enhar\",\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/#\\\/schema\\\/person\\\/62c964376839cf2c4b2eb682bf14d3cb\"},\"headline\":\"NVIDIA DGX Spark Ecosystem\",\"datePublished\":\"2026-08-24T12:34:27+00:00\",\"dateModified\":\"2026-08-24T12:35:19+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/nvidia-dgx-spark-ecosystem\\\/\"},\"wordCount\":3997,\"publisher\":{\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/nvidia-dgx-spark-ecosystem\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/wp-content\\\/uploads\\\/2026\\\/08\\\/ekosistem-blog-resim.webp\",\"articleSection\":[\"Generative AI\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/nvidia-dgx-spark-ecosystem\\\/\",\"url\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/nvidia-dgx-spark-ecosystem\\\/\",\"name\":\"NVIDIA DGX Spark Ecosystem - OpenZeka EN Blog\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/nvidia-dgx-spark-ecosystem\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/nvidia-dgx-spark-ecosystem\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/wp-content\\\/uploads\\\/2026\\\/08\\\/ekosistem-blog-resim.webp\",\"datePublished\":\"2026-08-24T12:34:27+00:00\",\"dateModified\":\"2026-08-24T12:35:19+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/nvidia-dgx-spark-ecosystem\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/nvidia-dgx-spark-ecosystem\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/nvidia-dgx-spark-ecosystem\\\/#primaryimage\",\"url\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/wp-content\\\/uploads\\\/2026\\\/08\\\/ekosistem-blog-resim.webp\",\"contentUrl\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/wp-content\\\/uploads\\\/2026\\\/08\\\/ekosistem-blog-resim.webp\",\"width\":1920,\"height\":1080},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/nvidia-dgx-spark-ecosystem\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"NVIDIA DGX Spark Ecosystem\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/#website\",\"url\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/\",\"name\":\"Aetherix B.V.\",\"description\":\"NVIDIA Jetson Developer Kits &amp;Edge Devices\",\"publisher\":{\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/#organization\",\"name\":\"Aetherix B.V.\",\"url\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/wp-content\\\/uploads\\\/2025\\\/06\\\/aetherix-site-icon.webp\",\"contentUrl\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/wp-content\\\/uploads\\\/2025\\\/06\\\/aetherix-site-icon.webp\",\"width\":421,\"height\":398,\"caption\":\"Aetherix B.V.\"},\"image\":{\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/profile.php?id=61576911356211\",\"https:\\\/\\\/x.com\\\/Aetherixnl\",\"https:\\\/\\\/www.instagram.com\\\/aetherixnl\\\/\",\"https:\\\/\\\/www.tiktok.com\\\/@aetherixnl\"],\"description\":\"Aetherix provides a full range of NVIDIA Jetson-based edge AI solutions\u2014including Developer Kits, AI Kits, industrial-grade Carrier Boards, and fully integrated Boxed AI Systems.\",\"email\":\"info@aetherix.com\",\"legalName\":\"Aetherix B.V.\",\"vatID\":\"NL867727688B01\"},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/blog.openzeka.com\\\/en\\\/#\\\/schema\\\/person\\\/62c964376839cf2c4b2eb682bf14d3cb\",\"name\":\"Enhar\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/189d567adce3bb0c8d438b4586bf861ec04980f2e451003975e3cf871781d0f4?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/189d567adce3bb0c8d438b4586bf861ec04980f2e451003975e3cf871781d0f4?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/189d567adce3bb0c8d438b4586bf861ec04980f2e451003975e3cf871781d0f4?s=96&d=mm&r=g\",\"caption\":\"Enhar\"}}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"NVIDIA DGX Spark Ecosystem - OpenZeka EN Blog","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:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/","og_locale":"en_US","og_type":"article","og_title":"NVIDIA DGX Spark Ecosystem","og_url":"https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/","og_site_name":"OpenZeka EN Blog","article_publisher":"https:\/\/www.facebook.com\/profile.php?id=61576911356211","article_published_time":"2026-08-24T12:34:27+00:00","article_modified_time":"2026-08-24T12:35:19+00:00","og_image":[{"width":1920,"height":1080,"url":"https:\/\/blog.openzeka.com\/en\/wp-content\/uploads\/2026\/08\/ekosistem-blog-resim.webp","type":"image\/webp"}],"author":"Enhar","twitter_card":"summary_large_image","twitter_creator":"@Aetherixnl","twitter_site":"@Aetherixnl","twitter_misc":{"Written by":"Enhar","Est. reading time":"14 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/#article","isPartOf":{"@id":"https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/"},"author":{"name":"Enhar","@id":"https:\/\/blog.openzeka.com\/en\/#\/schema\/person\/62c964376839cf2c4b2eb682bf14d3cb"},"headline":"NVIDIA DGX Spark Ecosystem","datePublished":"2026-08-24T12:34:27+00:00","dateModified":"2026-08-24T12:35:19+00:00","mainEntityOfPage":{"@id":"https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/"},"wordCount":3997,"publisher":{"@id":"https:\/\/blog.openzeka.com\/en\/#organization"},"image":{"@id":"https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/#primaryimage"},"thumbnailUrl":"https:\/\/blog.openzeka.com\/en\/wp-content\/uploads\/2026\/08\/ekosistem-blog-resim.webp","articleSection":["Generative AI"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/","url":"https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/","name":"NVIDIA DGX Spark Ecosystem - OpenZeka EN Blog","isPartOf":{"@id":"https:\/\/blog.openzeka.com\/en\/#website"},"primaryImageOfPage":{"@id":"https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/#primaryimage"},"image":{"@id":"https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/#primaryimage"},"thumbnailUrl":"https:\/\/blog.openzeka.com\/en\/wp-content\/uploads\/2026\/08\/ekosistem-blog-resim.webp","datePublished":"2026-08-24T12:34:27+00:00","dateModified":"2026-08-24T12:35:19+00:00","breadcrumb":{"@id":"https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/#primaryimage","url":"https:\/\/blog.openzeka.com\/en\/wp-content\/uploads\/2026\/08\/ekosistem-blog-resim.webp","contentUrl":"https:\/\/blog.openzeka.com\/en\/wp-content\/uploads\/2026\/08\/ekosistem-blog-resim.webp","width":1920,"height":1080},{"@type":"BreadcrumbList","@id":"https:\/\/blog.openzeka.com\/en\/nvidia-dgx-spark-ecosystem\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/blog.openzeka.com\/en\/"},{"@type":"ListItem","position":2,"name":"NVIDIA DGX Spark Ecosystem"}]},{"@type":"WebSite","@id":"https:\/\/blog.openzeka.com\/en\/#website","url":"https:\/\/blog.openzeka.com\/en\/","name":"Aetherix B.V.","description":"NVIDIA Jetson Developer Kits &amp;Edge Devices","publisher":{"@id":"https:\/\/blog.openzeka.com\/en\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/blog.openzeka.com\/en\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/blog.openzeka.com\/en\/#organization","name":"Aetherix B.V.","url":"https:\/\/blog.openzeka.com\/en\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/blog.openzeka.com\/en\/#\/schema\/logo\/image\/","url":"https:\/\/blog.openzeka.com\/en\/wp-content\/uploads\/2025\/06\/aetherix-site-icon.webp","contentUrl":"https:\/\/blog.openzeka.com\/en\/wp-content\/uploads\/2025\/06\/aetherix-site-icon.webp","width":421,"height":398,"caption":"Aetherix B.V."},"image":{"@id":"https:\/\/blog.openzeka.com\/en\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/profile.php?id=61576911356211","https:\/\/x.com\/Aetherixnl","https:\/\/www.instagram.com\/aetherixnl\/","https:\/\/www.tiktok.com\/@aetherixnl"],"description":"Aetherix provides a full range of NVIDIA Jetson-based edge AI solutions\u2014including Developer Kits, AI Kits, industrial-grade Carrier Boards, and fully integrated Boxed AI Systems.","email":"info@aetherix.com","legalName":"Aetherix B.V.","vatID":"NL867727688B01"},{"@type":"Person","@id":"https:\/\/blog.openzeka.com\/en\/#\/schema\/person\/62c964376839cf2c4b2eb682bf14d3cb","name":"Enhar","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/189d567adce3bb0c8d438b4586bf861ec04980f2e451003975e3cf871781d0f4?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/189d567adce3bb0c8d438b4586bf861ec04980f2e451003975e3cf871781d0f4?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/189d567adce3bb0c8d438b4586bf861ec04980f2e451003975e3cf871781d0f4?s=96&d=mm&r=g","caption":"Enhar"}}]}},"_links":{"self":[{"href":"https:\/\/blog.openzeka.com\/en\/wp-json\/wp\/v2\/posts\/1973","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.openzeka.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.openzeka.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.openzeka.com\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.openzeka.com\/en\/wp-json\/wp\/v2\/comments?post=1973"}],"version-history":[{"count":2,"href":"https:\/\/blog.openzeka.com\/en\/wp-json\/wp\/v2\/posts\/1973\/revisions"}],"predecessor-version":[{"id":1975,"href":"https:\/\/blog.openzeka.com\/en\/wp-json\/wp\/v2\/posts\/1973\/revisions\/1975"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.openzeka.com\/en\/wp-json\/wp\/v2\/media\/1976"}],"wp:attachment":[{"href":"https:\/\/blog.openzeka.com\/en\/wp-json\/wp\/v2\/media?parent=1973"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.openzeka.com\/en\/wp-json\/wp\/v2\/categories?post=1973"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.openzeka.com\/en\/wp-json\/wp\/v2\/tags?post=1973"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}