[{"data":1,"prerenderedAt":1411},["ShallowReactive",2],{"blog-post-en-\u002Fblog\u002Fnvidia-dsx-pod-vera-rubin-modular-data-centers":3,"blog-posts-sidebar-en":884},{"id":4,"title":5,"body":6,"categories":861,"date":868,"description":869,"extension":870,"heading":5,"image":871,"meta":872,"navigation":547,"originalUrl":873,"path":874,"seo":875,"slug":876,"socialImage":871,"stem":877,"updated":878,"video":879,"__hash__":883},"blog\u002Fblog\u002Fnvidia-dsx-pod-vera-rubin-modular-data-centers.md","Nvidia confirms the modular DC direction",{"type":7,"value":8,"toc":841},"minimark",[9,25,56,64,70,76,101,106,109,120,127,139,142,153,156,160,172,175,178,238,241,249,255,260,264,267,273,288,291,294,297,309,312,315,321,326,330,333,344,347,350,353,357,360,363,382,392,395,418,429,437,442,445,449,452,463,466,479,482,486,495,506,509,512,523,526,534,538,541,556,561,565,568,571,574,577,589,593,598,607,611,624,628,631,635],[10,11,12,16,17,20,21,24],"p",{},[13,14,15],"strong",{},"A modular data center should be quicker to deliver and practical to operate for years."," NVIDIA's newly announced ",[13,18,19],{},"DSX POD",", a reference design for ",[13,22,23],{},"Vera Rubin AI factories",", gives that conversation a significant boost. At ElioVP, we welcome it.",[10,26,27,28,31,32,35,36,39,40,43,44],{},"Announced on ",[13,29,30],{},"21 October 2026",", DSX POD brings factory integration and validation to partner-built modular containers. NVIDIA expects partner deployments in ",[13,33,34],{},"early 2027"," and says installation at a ",[13,37,38],{},"prepared site"," can take ",[13,41,42],{},"as little as 14 days",". That is a final deployment claim with an important condition, not a complete land-to-live project schedule.",[45,46,47],"sup",{},[48,49,55],"a",{"href":50,"ariaDescribedBy":51,"dataFootnoteRef":53,"id":54},"#user-content-fn-announcement",[52],"footnote-label","","user-content-fnref-announcement","1",[10,57,58,59,63],{},"For us, it reinforces the direction behind ",[48,60,62],{"href":61},"\u002Fproducts\u002Fdatacenters","ModFlex modular data centers",": build repeatable infrastructure, complete more work in the factory, and expand capacity through a coordinated plan. It also makes this a useful moment to explain why we moved from freight-container-based IT spaces toward purpose-built modular buildings.",[10,65,66,67],{},"The question we keep coming back to is straightforward: ",[13,68,69],{},"how will this place work after the delivery team has left?",[10,71,72],{},[73,74,75],"em",{},"Cover: editorial concept illustration, adapted to ElioVP's visual style. Not a photograph of a delivered facility or a DSX POD specification.",[10,77,78,81,82,86,87,86,91,95,96,100],{},[13,79,80],{},"In this article:"," ",[48,83,85],{"href":84},"#what-a-prepared-site-actually-includes","what a prepared site needs",", ",[48,88,90],{"href":89},"#why-we-chose-modular-buildings-for-the-it-space","why modular buildings",[48,92,94],{"href":93},"#liquid-cooling-still-needs-a-complete-heat-balance","the complete cooling picture",", and ",[48,97,99],{"href":98},"#watch-the-modflex-assembly","the ModFlex assembly video",".",[102,103,105],"h2",{"id":104},"what-nvidia-dsx-pod-brings-to-modular-ai-infrastructure","What NVIDIA DSX POD brings to modular AI infrastructure",[10,107,108],{},"Factory integration has a compelling logic. Equipment can be assembled under controlled conditions, interfaces checked earlier, and problems resolved before they become problems on a construction site.",[10,110,111,112],{},"NVIDIA brings that approach to a coordinated AI system. Its DSX POD design covers compute alongside networking, storage, electrical distribution, liquid cooling and transport requirements. More cabling, wiring, piping and functional validation happen before shipment. That helps an industry where many suppliers have to deliver one working installation.",[45,113,114],{},[48,115,119],{"href":116,"ariaDescribedBy":117,"dataFootnoteRef":53,"id":118},"#user-content-fn-technical",[52],"user-content-fnref-technical","2",[10,121,122],{},[123,124],"img",{"alt":125,"src":126},"NVIDIA DSX POD container cutaway showing integrated Vera Rubin infrastructure and internal services, supplied with the article draft.","\u002Fasset\u002Fimages\u002Fblog\u002Fnvidia-dsx-pod-modflex\u002Fdsx-pod.webp",[10,128,129],{},[73,130,131,132,138],{},"NVIDIA DSX POD rendering supplied with the draft. ",[48,133,137],{"href":134,"rel":135},"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fdata-center\u002Fproducts\u002Fdsx\u002Fpod\u002F",[136],"nofollow","NVIDIA's reference design",", not a ModFlex configuration.",[10,140,141],{},"A common design foundation can give customers and delivery partners a better starting point. The implementation can then reflect the location, operating model and infrastructure the customer needs.",[10,143,144,145],{},"NVIDIA's own FAQ recognizes that different construction approaches remain relevant. DSX POD initially targets large greenfield deployments with liquid cooling; conventional buildings and other modular approaches have their place too.",[45,146,147],{},[48,148,152],{"href":149,"ariaDescribedBy":150,"dataFootnoteRef":53,"id":151},"#user-content-fn-pod",[52],"user-content-fnref-pod","3",[10,154,155],{},"That is a healthy direction. Customers gain more options, and the discussion can become more specific about what each option delivers.",[102,157,159],{"id":158},"what-a-prepared-site-actually-includes","What a prepared site actually includes",[10,161,162,163,165,166],{},"The most consequential words in the 14-day deployment claim are ",[13,164,38],{},". Factory integration can shorten final installation, while the location still needs the infrastructure required to receive and operate the equipment.",[45,167,168],{},[48,169,55],{"href":50,"ariaDescribedBy":170,"dataFootnoteRef":53,"id":171},[52],"user-content-fnref-announcement-2",[10,173,174],{},"Available utility capacity must become a usable supply for the chosen IT system. Depending on the site, that means medium-voltage distribution, transformers, conversion equipment, protection, earthing, backup and the controls that coordinate them.",[10,176,177],{},"The physical work matters just as much. Foundations, drainage, equipment access and the lifting plan need to be ready. Cooling plant must have somewhere to reject heat. Network connections need tested routes. Permits and inspections must match the installation.",[179,180,181,194],"table",{},[182,183,184],"thead",{},[185,186,187,191],"tr",{},[188,189,190],"th",{},"Before the modules arrive",[188,192,193],{},"What needs to be resolved",[195,196,197,206,214,222,230],"tbody",{},[185,198,199,203],{},[200,201,202],"td",{},"Power",[200,204,205],{},"Available utility capacity, conversion, protection, backup and rack interfaces",[185,207,208,211],{},[200,209,210],{},"Cooling",[200,212,213],{},"Heat loads, external heat rejection, connections and local design temperatures",[185,215,216,219],{},[200,217,218],{},"Site and access",[200,220,221],{},"Foundations, drainage, delivery routes, lifting and replacement access",[185,223,224,227],{},[200,225,226],{},"Connectivity",[200,228,229],{},"Physical cable routes, network topology and tested connections",[185,231,232,235],{},[200,233,234],{},"Readiness to operate",[200,236,237],{},"Approvals, commissioning, maintenance procedures and agreed failure tests",[10,239,240],{},"A fast installation is valuable. The customer's project schedule also has to include the work that makes it possible.",[10,242,243,244,248],{},"With ModFlex, we bring site requirements, power, cooling, manufacture and commissioning into the same delivery plan, so site preparation can progress alongside the modules. The objective is to make the promised deployment date achievable. Our ",[48,245,247],{"href":246},"\u002Fblog\u002Fai-data-center-power-requirements-gpu-per-megawatt","AI data center power planning article"," explains why usable compute starts with the full facility, not a GPU count alone.",[10,250,251],{},[123,252],{"alt":253,"src":254},"Concept illustration showing a data center building, utility substation, outdoor cooling plant and access roads as one coordinated site.","\u002Fasset\u002Fimages\u002Fblog\u002Fnvidia-dsx-pod-modflex\u002Fsite-readiness.webp",[10,256,257],{},[73,258,259],{},"Site readiness brings utilities, cooling, access and the building together. Concept illustration, not a specified or completed project.",[102,261,263],{"id":262},"why-we-chose-modular-buildings-for-the-it-space","Why we chose modular buildings for the IT space",[10,265,266],{},"There are good reasons to use self-contained enclosures. We continue to see value in separate prefabricated units for electrical and mechanical infrastructure. For the space containing the IT equipment and the people maintaining it, our preference is a modular building arranged around the operation.",[10,268,269,272],{},[13,270,271],{},"NVIDIA describes purpose-built containers. Their dimensions and engineering should be judged on their own merits."," Our experience with ordinary freight-container layouts explains our building choice; it is not evidence about DSX POD's internal dimensions.",[10,274,275,276,279,280],{},"A typical 40-foot standard container in Hapag-Lloyd's fleet example is about ",[13,277,278],{},"2.35 metres wide internally",", before additional insulation or lining. Rack depth, working aisles, pipework and equipment replacement routes all compete for that space.",[45,281,282],{},[48,283,287],{"href":284,"ariaDescribedBy":285,"dataFootnoteRef":53,"id":286},"#user-content-fn-container",[52],"user-content-fnref-container","4",[10,289,290],{},"Modular rooms give us more flexibility over those decisions. We want room to remove equipment, isolate a cooling connection and work safely without turning every intervention into a space-management exercise.",[10,292,293],{},"A facility also needs a practical route between delivery, staging and the rack hall. Imagine receiving a replacement component during heavy rain. Where is it unpacked? Where is it checked? How does it reach the equipment without being exposed to the weather?",[10,295,296],{},"Those everyday questions shape the building layout. Our ModFlex examples bring rack halls, technical spaces, storage and staging into a coordinated facility, with dimensions and access routes shaped around the project.",[10,298,299,300,308],{},"The building envelope deserves the same attention. Insulation, thermal bridges and moisture control must suit the climate. Equipment needs appropriate temperature and humidity conditions, with condensation prevented. ASHRAE treats those environmental conditions as part of reliable operation.",[45,301,302],{},[48,303,307],{"href":304,"ariaDescribedBy":305,"dataFootnoteRef":53,"id":306},"#user-content-fn-ashrae",[52],"user-content-fnref-ashrae","5"," We also plan for escape routes, ventilation and the space required for the selected fire-protection systems.",[10,310,311],{},"That flexibility extends to the compute platform. ModFlex can be designed around NVIDIA, AMD or other AI and HPC systems, with rack loads and interfaces confirmed for the selected OEM configuration. This is project-specific engineering, not a blanket compatibility or certification claim.",[10,313,314],{},"We accept that additional space has a cost. Our choice weighs that cost against maintenance access, replacement logistics and the ability to accommodate future equipment.",[10,316,317],{},[123,318],{"alt":319,"src":320},"Example ModFlex layout with a purpose-built IT hall, separate electrical and mechanical units and external cooling equipment.","\u002Fasset\u002Fimages\u002Fblog\u002Fnvidia-dsx-pod-modflex\u002Fmodflex-facility.webp",[10,322,323],{},[73,324,325],{},"ModFlex arranges the building and supporting infrastructure around the intended deployment. Example configuration; final equipment, dimensions and capacity depend on the project.",[102,327,329],{"id":328},"transport-approval-and-building-design-need-separate-attention","Transport approval and building design need separate attention",[10,331,332],{},"The attraction of a shipping container includes familiar handling and transport arrangements. Once its structure or loading changes, those arrangements need to be checked against the final design.",[10,334,335,336],{},"Freight-container standards and the Convention for Safe Containers (CSC) address container specification and transport safety. Original approval cannot simply be assumed to cover structural alterations. Modified containers can be reassessed, tested and approved, with ratings changed where necessary.",[45,337,338],{},[48,339,343],{"href":340,"ariaDescribedBy":341,"dataFootnoteRef":53,"id":342},"#user-content-fn-transport",[52],"user-content-fnref-transport","6",[10,345,346],{},"A new door or large opening is therefore an engineering decision. The approved configuration determines the handling plan. Where standard container shipping is unsuitable, project-cargo options can be considered.",[10,348,349],{},"Rooftop cooling deserves the same practical scrutiny. A stacking rating is not a general approval to place plant on a roof. The support structure must carry the actual loads, accounting for wind, vibration, maintenance access and replacement.",[10,351,352],{},"Roof-mounted plant can be a sensible use of space when those requirements are designed in. A rendering cannot establish the load path, and the operating arrangement may differ from the transport arrangement.",[102,354,356],{"id":355},"liquid-cooling-still-needs-a-complete-heat-balance","Liquid cooling still needs a complete heat balance",[10,358,359],{},"Liquid cooling enables denser AI infrastructure. It also makes it important to define exactly which heat loads each cooling system handles.",[10,361,362],{},"A liquid-to-air split belongs to a particular equipment configuration. It can change with servers, power supplies, network equipment and storage, as well as with the boundary used for the calculation. A rack-level figure and a whole-facility figure do not necessarily describe the same thing.",[10,364,365,366,374],{},"NVIDIA describes MGX configurations capable of full liquid cooling. Other documented configurations retain air-cooled components: the DGX Vera Rubin NVL72 hardware guide lists air-cooled power supplies. Neither fact, on its own, establishes the heat balance of a particular DSX POD implementation.",[45,367,368],{},[48,369,373],{"href":370,"ariaDescribedBy":371,"dataFootnoteRef":53,"id":372},"#user-content-fn-mgx",[52],"user-content-fnref-mgx","7",[45,375,376],{},[48,377,381],{"href":378,"ariaDescribedBy":379,"dataFootnoteRef":53,"id":380},"#user-content-fn-dgx",[52],"user-content-fnref-dgx","8",[10,383,384,387,388,391],{},[13,385,386],{},"An illustrative calculation:"," if a 1 MW IT installation transfers 95% of its heat to liquid, the remaining 5% is still ",[13,389,390],{},"50 kW",". This is not a published DSX POD specification or a comparison with ModFlex. It shows why even a small remaining percentage deserves explicit cooling capacity.",[10,393,394],{},"There are distinct cooling jobs to account for:",[396,397,398,406,412],"ul",{},[399,400,401,402,405],"li",{},"The ",[13,403,404],{},"technology cooling circuit"," typically carries heat from the IT equipment to a coolant distribution unit (CDU).",[399,407,401,408,411],{},[13,409,410],{},"facility circuit"," carries heat from the CDU toward the external heat-rejection plant. A heat exchanger can keep the two coolant circuits separate.",[399,413,414,417],{},[13,415,416],{},"Room cooling"," removes heat released to the air. Its heat rejection may use shared or separate equipment, depending on the design.",[10,419,420,421],{},"NVIDIA's broader DSX facilities documentation includes liquid cooling and air handling for remaining equipment. That broader reference is not a POD-specific heat-balance guarantee.",[45,422,423],{},[48,424,428],{"href":425,"ariaDescribedBy":426,"dataFootnoteRef":53,"id":427},"#user-content-fn-facilities",[52],"user-content-fnref-facilities","9",[430,431],"prose-img",{"alt":432,"height":433,"png-fallback":434,"src":435,"width":436},"Two separate heat paths: IT cold plates transfer heat through a CDU heat exchanger to facility heat rejection; room-air heat passes through an air-cooling system to suitable heat rejection. Coolant circuits stay separate at the heat exchanger. Arrows show heat transfer, not fluid flow.",860,"\u002Fasset\u002Fimages\u002Fblog\u002Fnvidia-dsx-pod-modflex\u002Fheat-paths.png","\u002Fasset\u002Fimages\u002Fblog\u002Fnvidia-dsx-pod-modflex\u002Fheat-paths.svg",1600,[10,438,439],{},[73,440,441],{},"Liquid cooling and room cooling have distinct duties. Conceptual diagram; the actual heat balance, circuits and shared or separate heat-rejection equipment depend on the selected hardware and site. Select the image to enlarge it.",[10,443,444],{},"Our ModFlex process follows the selected hardware in the same way. Liquid cooling, room cooling, air handling and humidity control are sized around the deployment. We would not carry a thermal split from one configuration into another simply because both are modular.",[102,446,448],{"id":447},"the-location-still-shapes-cooling-and-power","The location still shapes cooling and power",[10,450,451],{},"Warmer coolant can expand the opportunity to use dry coolers and reduce mechanical refrigeration. The practical condition is that the site must have enough temperature difference to transfer the heat to the outside air.",[10,453,454,455],{},"Outdoor design temperature, heat-exchanger approach temperatures and water availability all affect the answer. A cooling arrangement suited to a cool climate may need more capacity or a different operating strategy in a hotter one. NVIDIA's own DSX cooling guidance retains a role for chillers in hotter conditions and for resilience.",[45,456,457],{},[48,458,462],{"href":459,"ariaDescribedBy":460,"dataFootnoteRef":53,"id":461},"#user-content-fn-climate",[52],"user-content-fnref-climate","10",[10,464,465],{},"The useful question is how the installation operates on the hot day it must tolerate, including the agreed maintenance or failure condition. That determines whether dry cooling alone, adiabatic assistance or mechanical cooling is appropriate.",[10,467,468,469,472,473],{},"Power needs similar attention. DSX POD specifies ",[13,470,471],{},"800 V DC at its core-container input",", with partners engineering the upstream supply and conversion for each region. It is not a statement that every local utility or every rack supplies that voltage.",[45,474,475],{},[48,476,119],{"href":116,"ariaDescribedBy":477,"dataFootnoteRef":53,"id":478},[52],"user-content-fnref-technical-2",[10,480,481],{},"A standardized interface can support repeatable manufacturing. The regional utility voltage, frequency, protection requirements and approval process still need to be addressed. Our approach starts at the available utility connection and works through to the selected rack interface, so customers can see what their site needs and how it will be maintained.",[102,483,485],{"id":484},"training-and-inference-ask-different-questions-of-the-facility","Training and inference ask different questions of the facility",[10,487,488,489],{},"NVIDIA positions DSX POD for model training and production inference. Multiple modules can form a training cluster, with connections between containers.",[45,490,491],{},[48,492,55],{"href":50,"ariaDescribedBy":493,"dataFootnoteRef":53,"id":494},[52],"user-content-fnref-announcement-3",[10,496,497,498],{},"For training, the facility layout must support the network architecture. Bandwidth, cable reach, switching topology and storage performance influence how effectively the GPUs work together. NVIDIA's cabling guidance calls for logical network design and physical layout to be developed together.",[45,499,500],{},[48,501,505],{"href":502,"ariaDescribedBy":503,"dataFootnoteRef":53,"id":504},"#user-content-fn-network",[52],"user-content-fnref-network","11",[10,507,508],{},"Modular construction gives us useful building blocks. We still need to place and connect them around the workload, whether they are containerized or assembled into a larger building.",[10,510,511],{},"Production inference brings service continuity into sharp focus. Customers experience an outage through a failed request or an unavailable application, regardless of how quickly the infrastructure was first installed.",[10,513,514,515],{},"We therefore want to understand what stays available when a power path, pump, cooling unit or network component is taken out of service. Redundant components must sit within an architecture that supports the required maintenance and failure behaviour. Uptime Institute's distinction between redundant capacity and concurrent maintainability is useful here.",[45,516,517],{},[48,518,522],{"href":519,"ariaDescribedBy":520,"dataFootnoteRef":53,"id":521},"#user-content-fn-uptime",[52],"user-content-fnref-uptime","12",[10,524,525],{},"The facility and software must also agree on where failures can spread. Several inference replicas sharing one cooling circuit or power dependency may still be exposed to the same interruption. The design should make those dependencies visible and provide the separation the service requires.",[10,527,528,529,533],{},"For ModFlex, resilience is a requirement defined with the customer and checked through the commissioning plan. The configuration follows the availability objective and operating model. Our ",[48,530,532],{"href":531},"\u002Fproducts\u002Fdatacenters#modflex-resilience","interactive power and cooling examples"," make those dependencies easier to explore; they are explanatory models, not certification of a delivered site.",[102,535,537],{"id":536},"watch-the-modflex-assembly","Watch the ModFlex assembly",[10,539,540],{},"This animation brings the modular building approach into view: an example facility takes shape as its structural elements and systems are assembled.",[10,542,543],{},[544,545],"video",{"id":546,"controls":547,"playsInline":547,"preload":548,"width":549,"height":550,"ariaLabel":551,"ariaDescribedBy":552,"poster":554,"src":555},"article-video",true,"none",1920,1080,"ModFlex modular data center assembly animation",[553],"modflex-assembly-caption","\u002Fasset\u002Fimages\u002Fblog\u002Fnvidia-dsx-pod-modflex\u002Fassembly-poster.webp","\u002Fasset\u002Fvideos\u002Fblog\u002Fnvidia-dsx-pod-modflex\u002Fmodflex-assembly.mp4",[10,557,558],{"id":553},[73,559,560],{},"69-second silent ModFlex concept animation supplied by ElioVP. Illustrative assembly sequence, not a construction timetable, footage of a completed facility or a NVIDIA DSX POD deployment.",[102,562,564],{"id":563},"a-welcome-reference-point-for-the-next-stage-of-modular","A welcome reference point for the next stage of modular",[10,566,567],{},"We see NVIDIA DSX POD as a positive development. More integration before delivery, repeatable interfaces and coordinated validation can help customers bring AI capacity online with greater confidence.",[10,569,570],{},"Our experience has led us toward purpose-built modular buildings because they give us flexibility over the whole facility: the IT space, the supporting infrastructure and the way people operate it.",[10,572,573],{},"The two ideas share an ambition. Factory work should make site delivery more predictable, and the completed installation should support the customer's workload for years.",[10,575,576],{},"A reference design gives that work a strong starting point. Readiness for a particular customer comes through detailed engineering, local approvals, site integration and commissioning. That is the role we built ModFlex around: turning available land, power and a capacity plan into a facility customers can operate, maintain and expand.",[10,578,579,584,585,100],{},[13,580,581],{},[48,582,583],{"href":61},"Explore ModFlex modular data centers"," or ",[48,586,588],{"href":587},"\u002Fcontact?enquiry=modular-data-center#contact-form","discuss your site and capacity requirements",[102,590,592],{"id":591},"frequently-asked-questions","Frequently asked questions",[594,595,597],"h3",{"id":596},"what-is-nvidia-dsx-pod","What is NVIDIA DSX POD?",[10,599,600,601],{},"DSX POD is NVIDIA's reference design for partner-built, factory-integrated modular Vera Rubin AI factories, announced on 21 October 2026. NVIDIA expects deployments in early 2027; the reference design is not itself a completed, approved facility at every customer site.",[45,602,603],{},[48,604,55],{"href":50,"ariaDescribedBy":605,"dataFootnoteRef":53,"id":606},[52],"user-content-fnref-announcement-4",[594,608,610],{"id":609},"does-the-14-day-deployment-claim-include-site-preparation","Does the 14-day deployment claim include site preparation?",[10,612,613,614,617,618],{},"No. NVIDIA specifies deployment in as little as 14 days ",[13,615,616],{},"at a prepared site",". Utility connections, civil works, cooling provision, permits and other project-specific readiness work must be accounted for separately.",[45,619,620],{},[48,621,55],{"href":50,"ariaDescribedBy":622,"dataFootnoteRef":53,"id":623},[52],"user-content-fnref-announcement-5",[594,625,627],{"id":626},"is-modflex-the-same-product-as-dsx-pod","Is ModFlex the same product as DSX POD?",[10,629,630],{},"No. ModFlex is ElioVP's modular data center approach, with purpose-built IT spaces and supporting power and cooling designed around the project. This article is our engineering perspective on NVIDIA's announcement, not a claim that ModFlex is NVIDIA DSX POD-certified or part of that launch program.",[102,632,634],{"id":633},"sources-and-further-reading","Sources and further reading",[636,637,640,645],"section",{"className":638,"dataFootnotes":53},[639],"footnotes",[102,641,644],{"className":642,"id":52},[643],"sr-only","Footnotes",[646,647,648,692,712,724,737,750,763,776,789,802,815,828],"ol",{},[399,649,651,656,657,81,664,81,671,81,678,81,685],{"id":650},"user-content-fn-announcement",[48,652,655],{"href":653,"rel":654},"https:\u002F\u002Fnvidianews.nvidia.com\u002Fnews\u002Fnvidia-introduces-dsx-pod-ai-factories-to-accelerate-europes-infrastructure-deployment",[136],"NVIDIA Newsroom: DSX POD AI factories, 21 October 2026",". ",[48,658,663],{"href":659,"ariaLabel":660,"className":661,"dataFootnoteBackref":53},"#user-content-fnref-announcement","Back to reference 1",[662],"data-footnote-backref","↩",[48,665,663,669],{"href":666,"ariaLabel":667,"className":668,"dataFootnoteBackref":53},"#user-content-fnref-announcement-2","Back to reference 1-2",[662],[45,670,119],{},[48,672,663,676],{"href":673,"ariaLabel":674,"className":675,"dataFootnoteBackref":53},"#user-content-fnref-announcement-3","Back to reference 1-3",[662],[45,677,152],{},[48,679,663,683],{"href":680,"ariaLabel":681,"className":682,"dataFootnoteBackref":53},"#user-content-fnref-announcement-4","Back to reference 1-4",[662],[45,684,287],{},[48,686,663,690],{"href":687,"ariaLabel":688,"className":689,"dataFootnoteBackref":53},"#user-content-fnref-announcement-5","Back to reference 1-5",[662],[45,691,307],{},[399,693,695,656,700,81,705],{"id":694},"user-content-fn-technical",[48,696,699],{"href":697,"rel":698},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fachieve-productive-ai-capacity-faster-and-more-predictably-with-nvidia-dsx-pod\u002F",[136],"NVIDIA Technical Blog: productive AI capacity with DSX POD",[48,701,663],{"href":702,"ariaLabel":703,"className":704,"dataFootnoteBackref":53},"#user-content-fnref-technical","Back to reference 2",[662],[48,706,663,710],{"href":707,"ariaLabel":708,"className":709,"dataFootnoteBackref":53},"#user-content-fnref-technical-2","Back to reference 2-2",[662],[45,711,119],{},[399,713,715,656,719],{"id":714},"user-content-fn-pod",[48,716,718],{"href":134,"rel":717},[136],"NVIDIA DSX POD product overview and FAQ",[48,720,663],{"href":721,"ariaLabel":722,"className":723,"dataFootnoteBackref":53},"#user-content-fnref-pod","Back to reference 3",[662],[399,725,727,656,732],{"id":726},"user-content-fn-container",[48,728,731],{"href":729,"rel":730},"https:\u002F\u002Fwww.hapag-lloyd.com\u002Fen\u002Fservices-information\u002Fcargo-fleet\u002Fcontainer\u002F40-standard.html",[136],"Hapag-Lloyd: example 40-foot standard container dimensions",[48,733,663],{"href":734,"ariaLabel":735,"className":736,"dataFootnoteBackref":53},"#user-content-fnref-container","Back to reference 4",[662],[399,738,740,656,745],{"id":739},"user-content-fn-ashrae",[48,741,744],{"href":742,"rel":743},"https:\u002F\u002Fhandbook.ashrae.org\u002FHandbooks\u002FA23\u002FSI\u002FA23_Ch20\u002Fa23_ch20_si.aspx",[136],"ASHRAE Handbook: data centers and telecommunication facilities",[48,746,663],{"href":747,"ariaLabel":748,"className":749,"dataFootnoteBackref":53},"#user-content-fnref-ashrae","Back to reference 5",[662],[399,751,753,656,758],{"id":752},"user-content-fn-transport",[48,754,757],{"href":755,"rel":756},"https:\u002F\u002Fwww.irclass.org\u002Fmedia\u002F5475\u002Fguidelines-on-certification-of-containers_rev-0_april-2021.pdf",[136],"Indian Register of Shipping: container certification and reassessment after modification, section 2.7",[48,759,663],{"href":760,"ariaLabel":761,"className":762,"dataFootnoteBackref":53},"#user-content-fnref-transport","Back to reference 6",[662],[399,764,766,656,771],{"id":765},"user-content-fn-mgx",[48,767,770],{"href":768,"rel":769},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fnvidia-vera-rubin-pod-seven-chips-five-rack-scale-systems-one-ai-supercomputer\u002F",[136],"NVIDIA: Vera Rubin, MGX and rack-scale systems",[48,772,663],{"href":773,"ariaLabel":774,"className":775,"dataFootnoteBackref":53},"#user-content-fnref-mgx","Back to reference 7",[662],[399,777,779,656,784],{"id":778},"user-content-fn-dgx",[48,780,783],{"href":781,"rel":782},"https:\u002F\u002Fdocs.nvidia.com\u002Fdgx\u002Fdgxvr-user-guide\u002Fhardware.html#power-shelves",[136],"NVIDIA DGX Vera Rubin NVL72 hardware guide: power shelves",[48,785,663],{"href":786,"ariaLabel":787,"className":788,"dataFootnoteBackref":53},"#user-content-fnref-dgx","Back to reference 8",[662],[399,790,792,656,797],{"id":791},"user-content-fn-facilities",[48,793,796],{"href":794,"rel":795},"https:\u002F\u002Fdocs.nvidia.com\u002Fdsx\u002Ffacilities-infra\u002Freference-design-overview",[136],"NVIDIA DSX facilities infrastructure reference overview",[48,798,663],{"href":799,"ariaLabel":800,"className":801,"dataFootnoteBackref":53},"#user-content-fnref-facilities","Back to reference 9",[662],[399,803,805,656,810],{"id":804},"user-content-fn-climate",[48,806,809],{"href":807,"rel":808},"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002F?p=121508",[136],"NVIDIA DSX MaxLPS cooling guidance",[48,811,663],{"href":812,"ariaLabel":813,"className":814,"dataFootnoteBackref":53},"#user-content-fnref-climate","Back to reference 10",[662],[399,816,818,656,823],{"id":817},"user-content-fn-network",[48,819,822],{"href":820,"rel":821},"https:\u002F\u002Fdocs.nvidia.com\u002Fdgx-superpod\u002Fdesign-guide-cabling-data-centers\u002Flatest\u002Fconsiderations.html",[136],"NVIDIA DGX SuperPOD cabling design: logical and physical considerations",[48,824,663],{"href":825,"ariaLabel":826,"className":827,"dataFootnoteBackref":53},"#user-content-fnref-network","Back to reference 11",[662],[399,829,831,656,836],{"id":830},"user-content-fn-uptime",[48,832,835],{"href":833,"rel":834},"https:\u002F\u002Fuptimeinstitute.com\u002Ftiers",[136],"Uptime Institute: Tier classification and concurrent maintainability",[48,837,663],{"href":838,"ariaLabel":839,"className":840,"dataFootnoteBackref":53},"#user-content-fnref-uptime","Back to reference 12",[662],{"title":53,"searchDepth":842,"depth":842,"links":843},2,[844,845,846,847,848,849,850,851,852,853,859,860],{"id":104,"depth":842,"text":105},{"id":158,"depth":842,"text":159},{"id":262,"depth":842,"text":263},{"id":328,"depth":842,"text":329},{"id":355,"depth":842,"text":356},{"id":447,"depth":842,"text":448},{"id":484,"depth":842,"text":485},{"id":536,"depth":842,"text":537},{"id":563,"depth":842,"text":564},{"id":591,"depth":842,"text":592,"children":854},[855,857,858],{"id":596,"depth":856,"text":597},3,{"id":609,"depth":856,"text":610},{"id":626,"depth":856,"text":627},{"id":633,"depth":842,"text":634},{"id":52,"depth":842,"text":644},[862,863,864,865,866,867],"Modular DC","AI Infrastructure","ModFlex","NVIDIA","Vera Rubin","Liquid Cooling","2026-10-21T13:00:00Z","NVIDIA DSX POD brings Vera Rubin AI factories to modular data centers. ElioVP explains site readiness, cooling and the thinking behind ModFlex.","md","\u002Fasset\u002Fimages\u002Fblog\u002Fnvidia-dsx-pod-modflex\u002Fhero.webp",{},"https:\u002F\u002Feliovp.com\u002Fblog\u002Fnvidia-dsx-pod-vera-rubin-modular-data-centers","\u002Fblog\u002Fnvidia-dsx-pod-vera-rubin-modular-data-centers",{"title":5,"description":869},"nvidia-dsx-pod-vera-rubin-modular-data-centers","blog\u002Fnvidia-dsx-pod-vera-rubin-modular-data-centers",null,{"name":880,"description":881,"src":555,"poster":554,"duration":882,"uploadDate":868},"ModFlex modular data center assembly","A 69-second silent concept animation showing the assembly of an example ModFlex modular data center. Illustrative sequence, not a construction timetable or a DSX POD deployment.","PT1M9S","KfP5G349aq77j6pTHDo_onLufs1u9OkeWI5vmY2SQW8",[885,887,901,910,919,929,942,952,964,973,988,997,1008,1040,1051,1073,1091,1110,1128,1145,1162,1174,1190,1205,1217,1226,1234,1249,1261,1272,1283,1293,1306,1316,1329,1340,1350,1361,1370,1382,1393,1402],{"path":874,"title":5,"description":869,"date":868,"slug":876,"image":871,"originalUrl":873,"categories":886},[862,863,864,865,866,867],{"path":888,"title":889,"description":890,"date":891,"slug":892,"image":893,"originalUrl":894,"categories":895},"\u002Fblog\u002Fpaiton-qwen38-flash-next-4bit-radeon-ai-pro-r9700","Qwen3.8 Flash Next in 4-bit on 2 × Radeon R9700: tested against 16-bit","Paiton runs Qwen3.8 Flash Next in 4-bit on two R9700 cards: 96.8% GSM8K, 96.3% HumanEval, 189.8 tokens\u002Fs and an optional RAM prefix cache. Accuracy, speed and setup.","2026-10-12T15:00:00Z","paiton-qwen38-flash-next-4bit-radeon-ai-pro-r9700","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-qwen38-flash-next-4bit\u002Fhero.webp","https:\u002F\u002Feliovp.com\u002Fblog\u002Fpaiton-qwen38-flash-next-4bit-radeon-ai-pro-r9700",[896,897,898,899,900],"Paiton","AMD Radeon","Local AI","Qwen","Quantization",{"path":902,"title":903,"description":904,"date":905,"slug":906,"image":907,"originalUrl":908,"categories":909},"\u002Fblog\u002Fpaiton-qwen38-flash-next-radeon-ai-pro-r9700","Qwen3.8 Flash Next on 2 × Radeon AI PRO R9700: 216 tokens\u002Fs, 200K context","Paiton runs Qwen3.8 Flash Next with 3-bit experts on two R9700 cards: 216.3 tokens\u002Fs, 565.1 aggregate tokens\u002Fs and a separate 200K mode. Benchmarks, quality and setup.","2026-10-10T10:30:00Z","paiton-qwen38-flash-next-radeon-ai-pro-r9700","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-qwen38-flash-next\u002Fhero.webp","https:\u002F\u002Feliovp.com\u002Fblog\u002Fpaiton-qwen38-flash-next-radeon-ai-pro-r9700",[896,897,898,899,900],{"path":911,"title":912,"description":913,"date":914,"slug":915,"image":916,"originalUrl":917,"categories":918},"\u002Fblog\u002Fpaiton-qwen38-w3a4-radeon-ai-pro-r9700","Qwen3.8 27B on 1 × Radeon AI PRO R9700: 3-bit weights, 20% faster decode","Qwen3.8 27B on one R9700: 19.9% faster weighted decode, a new 4-bit cache, 200K context and optional vision. Benchmarks and current Paiton setup.","2026-09-27T09:00:00Z","paiton-qwen38-w3a4-radeon-ai-pro-r9700","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-qwen38-w3a4\u002Fhero.webp","https:\u002F\u002Feliovp.com\u002Fblog\u002Fpaiton-qwen38-w3a4-radeon-ai-pro-r9700",[896,897,898,899,900],{"path":920,"title":921,"description":922,"date":923,"slug":924,"image":925,"originalUrl":926,"categories":927},"\u002Fblog\u002Fpaiton-qwen-image-21-radeon-ai-pro-r9700","Qwen-Image 2.1 on 1 × Radeon AI PRO R9700: 2048×2048 images in 103 seconds","Generate 2048×2048 Qwen-Image 2.1 images locally with 1 × Radeon AI PRO R9700. The released Paiton v1.0.2 container measured 103.29 seconds per warm request, through PNG delivery.","2026-09-23T09:00:00Z","paiton-qwen-image-21-radeon-ai-pro-r9700","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-qwen-image-21\u002Fhero.webp","https:\u002F\u002Feliovp.com\u002Fblog\u002Fpaiton-qwen-image-21-radeon-ai-pro-r9700",[896,897,898,928,899],"Image Generation",{"path":930,"title":931,"description":932,"date":933,"slug":934,"image":935,"originalUrl":936,"categories":937},"\u002Fblog\u002Fpaiton-qwen38-mxfp4-dflash2-r9700","Qwen3.8: 400.7 tok\u002Fs on R9700 | Paiton","Qwen3.8 on one R9700: 400.7 aggregate tok\u002Fs with ROCm 10 and vLLM 0.29, plus public 200K\u002F220K chat profiles. Benchmarks, limits and launch commands.","2026-09-16T07:30:00Z","paiton-qwen38-mxfp4-dflash2-r9700","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-qwen38-mxfp4\u002Fupdate-2026-09-19\u002Fhero.webp","https:\u002F\u002Feliovp.com\u002Fblog\u002Fpaiton-qwen38-mxfp4-dflash2-r9700",[896,897,938,939,940,941],"vLLM","Qwen3.8","Inference Optimization","DFlash2",{"path":943,"title":944,"description":945,"date":946,"slug":947,"image":948,"originalUrl":949,"categories":950},"\u002Fblog\u002Fpaiton-qwen38-neo-gguf-vllm-r9700","Qwen3.8 GGUF in vLLM: Faster Responses on One Radeon","Run the original NEO CODER MAX GGUF in vLLM with Paiton on an R9700. Explore measured latency gains, image input and local deployment.","2026-09-14T07:30:00Z","paiton-qwen38-neo-gguf-vllm-r9700","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-neo-gguf\u002F00-hero-neo-gguf-r9700.webp","https:\u002F\u002Feliovp.com\u002Fblog\u002Fpaiton-qwen38-neo-gguf-vllm-r9700",[896,897,898,951,938],"GGUF",{"path":953,"title":954,"description":955,"date":956,"slug":957,"image":958,"originalUrl":959,"categories":960},"\u002Fblog\u002Fpaiton-minimax-h3-radeon-ai-pro-r9700","MiniMax H3 on Radeon: 15-Second Video With Native Sound","Paiton generates a 15-second MiniMax H3 video with stereo audio on one Radeon AI PRO R9700 in 5m 33s, with 16.7% lower latency than matched stock.","2026-09-09T07:30:00Z","paiton-minimax-h3-radeon-ai-pro-r9700","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-minimax-h3\u002F00-featured-minimax-h3-r9700.webp","https:\u002F\u002Feliovp.com\u002Fblog\u002Fpaiton-minimax-h3-radeon-ai-pro-r9700",[896,897,898,961,962,963],"Video Generation","MiniMax H3","ComfyUI",{"path":965,"title":966,"description":967,"date":968,"slug":969,"image":970,"originalUrl":965,"categories":971},"\u002Fblog\u002Fpaiton-flux2-klein-radeon-ai-pro-r9700","Local FLUX.2 klein on Radeon AI PRO R9700: Faster Image Generation with Less VRAM","Paiton generates 1024×1024 FLUX.2 klein images in 1.054 seconds on an R9700: 16.2% lower warm latency and 33.4% lower peak Torch allocation.","2026-09-07T09:00:00","paiton-flux2-klein-radeon-ai-pro-r9700","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-flux2-klein\u002Ffox-paiton.webp",[896,897,898,928,972,963],"FLUX",{"path":974,"title":975,"description":976,"date":977,"slug":978,"image":979,"originalUrl":980,"categories":981},"\u002Fblog\u002Fpaiton-ornith15-radeon-ai-pro-r9700","Ornith 1.5 at 44.6 tok\u002Fs on One Radeon AI PRO R9700","Paiton serves Ornith 1.5 35B A3B at 44.63 output tok\u002Fs on one Radeon AI PRO R9700, 27% faster than tuned stock vLLM, with 21.3% lower modeled cost.","2026-09-05T09:00:00","paiton-ornith15-radeon-ai-pro-r9700","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-ornith15\u002F00-featured-ornith15-r9700.webp","https:\u002F\u002Feliovp.com\u002Fblog\u002Fpaiton-ornith15-radeon-ai-pro-r9700",[896,982,897,983,984,985,940,986,938,987],"Artificial Intelligence","AI Inference","GPU Performance","Inference Latency","Large Language Models","Cost Efficiency",{"path":989,"title":990,"description":991,"date":992,"slug":993,"image":994,"originalUrl":995,"categories":996},"\u002Fblog\u002Fpaiton-qwen38-radeon-ai-pro-r9700","Paiton: 21% More Qwen3.8 Throughput on Radeon AI PRO R9700","Paiton serves AMD’s Qwen3.8 27B at 39.77 output tokens\u002Fs on one Radeon AI PRO R9700, delivering 21% more throughput and 17.4% lower modeled cost.","2026-09-04T09:00:00","paiton-qwen38-radeon-ai-pro-r9700","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-r9700\u002F00-featured-paiton-r9700.webp","https:\u002F\u002Feliovp.com\u002Fblog\u002Fpaiton-qwen38-radeon-ai-pro-r9700",[896,982,897,983,984,985,940,986,938,987],{"path":246,"title":998,"description":999,"date":1000,"slug":1001,"image":1002,"originalUrl":878,"categories":1003},"AI Data Center Power Requirements: The GPU\u002FMW Illusion","Why do AI data-center proposals quote different GPU capacities? See how PUE, peak loads, storage, networking and cooling determine deployable compute.","2026-07-27T23:52:00","ai-data-center-power-requirements-gpu-per-megawatt","\u002Fasset\u002Fimages\u002Fblog\u002Fai-data-center-power-requirements-gpu-per-megawatt\u002Fgpu-per-megawatt-illusion.webp",[1004,863,1005,864,1006,1007],"All","Data Centers","HPC","AMD Helios",{"path":1009,"title":1010,"description":1011,"date":1012,"slug":1013,"image":1014,"originalUrl":1015,"categories":1016},"\u002Fblog\u002Fpaiton-returns-to-its-diffusion-roots-optimizing-wan2-2-t2v-a14b-on-amd-mi355x","Wan2.2 Video Generation: Paiton on AMD MI355X","Compare Wan2.2-T2V-A14B video generation on AMD MI355X with Paiton and NVIDIA B200 using Diffusers, and explore our diffusion optimization approach.","2026-06-10T14:04:04","paiton-returns-to-its-diffusion-roots-optimizing-wan2-2-t2v-a14b-on-amd-mi355x","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fpaitonwan2.webp","https:\u002F\u002Feliovp.com\u002Fpaiton-returns-to-its-diffusion-roots-optimizing-wan2-2-t2v-a14b-on-amd-mi355x\u002F",[1004,982,896,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,896,1034,1035,1036,1037,1038,1039],"14B","AMD","B200","Benchmarks","Blackwell","Compute","Diffusion","Eliovp","Generative AI","GPU","Hardware","Inference","Instinct","MI355x","NVidia","On-Premise","Optimization","Sovereign AI","T2V","Text-to-Video","Tuning","Video-Generation","Wan2.2",{"path":1041,"title":1042,"description":1043,"date":1044,"slug":1045,"image":1046,"originalUrl":1047,"categories":1048},"\u002Fblog\u002Ffrom-the-attic-to-the-front-page-eliovp-recognized-as-a-pioneer-in-chip-optimization-data-center-infrastructure","ElioVP in De Tijd: Chip Optimization and Data Centers","Read about De Tijd's coverage of ElioVP, from its origins in chip optimization to its work on modular data centers and high-density cooling.","2026-02-10T20:48:12","from-the-attic-to-the-front-page-eliovp-recognized-as-a-pioneer-in-chip-optimization-data-center-infrastructure","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fphysicalnewspaper.webp","https:\u002F\u002Feliovp.com\u002Ffrom-the-attic-to-the-front-page-eliovp-recognized-as-a-pioneer-in-chip-optimization-data-center-infrastructure\u002F",[1004,982,862,1049,1018,1050,862,1031],"Uncategorized","De Tijd",{"path":1052,"title":1053,"description":1054,"date":1055,"slug":1056,"image":1057,"originalUrl":1058,"categories":1059},"\u002Fblog\u002Fprivacy-is-geen-it-probleem-meer-het-is-een-strategische-prioriteit","AI Privacy: A Strategic Priority for Benelux Businesses","Explore generative AI privacy risks, trust, data retention and governance, and why Benelux businesses need a strategic approach to secure AI.","2026-01-29T13:51:11","privacy-is-geen-it-probleem-meer-het-is-een-strategische-prioriteit","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fheaderimage.webp","https:\u002F\u002Feliovp.com\u002Fprivacy-is-geen-it-probleem-meer-het-is-een-strategische-prioriteit\u002F",[1004,982,1060,1049,1061,1062,1063,1064,1065,1066,1067,1068,1024,1069,1025,1070,1071,1072],"Trending","AI Act","Anthropomorphism","AVG","Benelux","ChatGPT","Cybersecurity","Data Governance","Data Security","GDPR","Microsoft Copilot","Privacy","Shadow AI",{"path":1074,"title":1075,"description":1076,"date":1077,"slug":1078,"image":1079,"originalUrl":1080,"categories":1081},"\u002Fblog\u002Fitsme-bij-ons-is-het-its-not-me-en-dit-is-waarom","Why We Do Not Use itsme: Privacy and Data Sovereignty","Why ElioVP does not use itsme: our assessment of identity metadata, cloud dependence, data sovereignty and authentication risks.","2025-11-27T09:32:14","itsme-bij-ons-is-het-its-not-me-en-dit-is-waarom","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Ffrontimage.webp","https:\u002F\u002Feliovp.com\u002Fitsme-bij-ons-is-het-its-not-me-en-dit-is-waarom\u002F",[1004,1082,1083,1084,1066,1085,1086,1087,1069,1088,1089,1090,1071],"AWS","Belgian Mobile ID","Cloud Act","Data Sovereignty","Digital Identity","eIDAS","itsme","Liberty Global","MyGov.be",{"path":1092,"title":1093,"description":1094,"date":1095,"slug":1096,"image":1097,"originalUrl":1098,"categories":1099},"\u002Fblog\u002Ffield-report-the-reality-of-building-agentic-ai-in-2025","Field Report. The Reality of Building Agentic AI in 2025","Lessons from building on-premise AI agents in 2025 cover workflow design, observability, model training, hallucinations and GPU memory limits.","2025-11-25T14:03:39","field-report-the-reality-of-building-agentic-ai-in-2025","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Ffieldreport.webp","https:\u002F\u002Feliovp.com\u002Ffield-report-the-reality-of-building-agentic-ai-in-2025\u002F",[1004,982,1100,1060,1101,1102,1103,1104,1105,1106,1107,1108,1034,1109],"Solutions","Agentic AI","AI Engineering","AI Strategy","Autonomous Agents","Enterprise AI","Local LLM","Model Fine-Tuning","On-Premise AI","VRAM Optimization",{"path":1111,"title":1112,"description":1113,"date":1114,"slug":1115,"image":1116,"originalUrl":1117,"categories":1118},"\u002Fblog\u002Fthe-synthetic-unicorn-bubble","The Synthetic Unicorn Bubble","An analysis of AI neocloud investment risks, examining circular financing, infrastructure claims, contract terms and due diligence.","2025-11-24T19:22:28","the-synthetic-unicorn-bubble","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fsyntheticunicorn.webp","https:\u002F\u002Feliovp.com\u002Fthe-synthetic-unicorn-bubble\u002F",[1004,982,1060,863,1119,1120,1121,1122,1123,1124,1125,1126,1127],"AI Neocloud","Circular Financing","GPU Cloud","Investment Risks","Startup Valuation","Synthetic Bubble","Tech Analysis","Vaporware","Venture Capital",{"path":1129,"title":1130,"description":1131,"date":1132,"slug":1133,"image":1134,"originalUrl":1135,"categories":1136},"\u002Fblog\u002Fbuilding-the-engine-for-the-ai-race-the-4-month-path-to-nvidia-gb300-nvl72-power","NVIDIA GB300 NVL72: A Four-Month Modular Data Center Plan","Explore a modular data center design for NVIDIA GB300 NVL72, covering redundant power, hybrid cooling and a four-month deployment plan.","2025-11-20T14:10:19","building-the-engine-for-the-ai-race-the-4-month-path-to-nvidia-gb300-nvl72-power","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fsuperpodmodflexfrontimage.webp","https:\u002F\u002Feliovp.com\u002Fbuilding-the-engine-for-the-ai-race-the-4-month-path-to-nvidia-gb300-nvl72-power\u002F",[1004,862,1049,1137,863,1138,1139,867,1140,1141,1142,1143,1144],"150kW Rack","DLC","High Density","Modular Data Center","NVIDIA Blackwell Ultra","NVIDIA GB300","NVL72","Rapid Deployment",{"path":1146,"title":1147,"description":1148,"date":1149,"slug":1150,"image":1151,"originalUrl":1152,"categories":1153},"\u002Fblog\u002Fwhy-cuda-translation-wont-unlock-amds-real-potential","Why “CUDA” Translation Won’t Unlock AMD’s Real Potential","Why CUDA compatibility is not the same as AMD performance: explore ROCm, HIP, kernel tuning and the case for hardware-specific optimization.","2025-11-12T14:48:37","why-cuda-translation-wont-unlock-amds-real-potential","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fchatgpt-image-nov-11-2025-09_16_10-pm-1.webp","https:\u002F\u002Feliovp.com\u002Fwhy-cuda-translation-wont-unlock-amds-real-potential\u002F",[1004,982,896,1049,1154,982,1155,1156,1157,1158,1159,1160,896,1161],"AMD MI300X","CUDA Translation","FP8","GPU Optimization","High Performance Computing","HIP","Kernel Tuning","ROCm",{"path":1163,"title":1164,"description":1165,"date":1166,"slug":1167,"image":1168,"originalUrl":1169,"categories":1170},"\u002Fblog\u002Fpaiton-the-simplest-way-to-supercharge-ai-inference","Paiton: The Simplest Way to Supercharge AI Inference","Learn how Paiton integrates with existing inference stacks, with AMD MI300X benchmark results and performance-per-dollar comparisons.","2025-11-11T10:31:22","paiton-the-simplest-way-to-supercharge-ai-inference","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fpaiton-powaaah.webp","https:\u002F\u002Feliovp.com\u002Fpaiton-the-simplest-way-to-supercharge-ai-inference\u002F",[1004,982,896,983,1171,1154,987,1172,940,1160,896,1173,938],"AMD Instinct","High Throughput","SGLang",{"path":1175,"title":1176,"description":1177,"date":1178,"slug":1179,"image":1180,"originalUrl":1181,"categories":1182},"\u002Fblog\u002Fstop-overpaying-paiton-mi300x-moe-beats-h200-b200-on-1m-tokens","Paiton MoE Benchmarks: MI300X vs H200 and B200","Compare Qwen3-30B-A3B MoE inference with Paiton on MI300X against H200 and B200, including throughput and cost per million tokens.","2025-09-26T13:36:18","stop-overpaying-paiton-mi300x-moe-beats-h200-b200-on-1m-tokens","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fhulkvshulkpaitonwins.webp","https:\u002F\u002Feliovp.com\u002Fstop-overpaying-paiton-mi300x-moe-beats-h200-b200-on-1m-tokens\u002F",[1004,982,896,1183,1154,1184,940,1185,1186,1187,1188,896,1189],"AI Benchmarks","Cost per Token","Mixture of Experts","MoE","NVIDIA B200","NVIDIA H200","Qwen3",{"path":1191,"title":1192,"description":1193,"date":1194,"slug":1195,"image":1196,"originalUrl":1197,"categories":1198},"\u002Fblog\u002Fagentic-ai-but-make-it-local-from-inbox-to-insight-to-action-en","Local Agentic AI: From Inbox to Action","Local-first AI agents turn email, documents and images into tickets, reports and actions, using models tailored to your data and systems.","2025-09-16T13:09:00","agentic-ai-but-make-it-local-from-inbox-to-insight-to-action-en","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Ffrontfotoblog.webp","https:\u002F\u002Feliovp.com\u002Fagentic-ai-but-make-it-local-from-inbox-to-insight-to-action-en\u002F",[1004,982,1100,1049,1101,1199,1200,1201,1202,1106,1108,1034,1203,1204],"Damage Detection","Document Processing","Email Automation","Invoice Extraction","Ticket Automation","Workflow Automation",{"path":1206,"title":1207,"description":1208,"date":1209,"slug":1210,"image":1211,"originalUrl":1212,"categories":1213},"\u002Fblog\u002Fmi300x-fp8-data-parallel-benchmarks-8-64-gpus-h200-left-behind-b200-within-reach","MI300X FP8 Benchmarks: GPU Partitioning with Paiton","Explore Llama 3.1 8B FP8 benchmarks on partitioned MI300X GPUs with Paiton, comparing throughput and latency against NVIDIA H200 and B200.","2025-07-31T13:32:57","mi300x-fp8-data-parallel-benchmarks-8-64-gpus-h200-left-behind-b200-within-reach","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fimage-2-1.webp","https:\u002F\u002Feliovp.com\u002Fmi300x-fp8-data%e2%80%91parallel-benchmarks-8-64-gpus-h200-left-behind-b200-within-reach\u002F",[1004,982,896,1049,1214,1018,1019,1215,1216,1030,1031,896,938],"AI","H200","MI300X",{"path":1218,"title":1219,"description":1220,"date":1221,"slug":1222,"image":1223,"originalUrl":1224,"categories":1225},"\u002Fblog\u002Fapplicable-ai-for-businesses","Applicable AI for Businesses","Explore ElioVP's approach to local AI for business workflows, including custom model training and automated damage detection for logistics.","2025-07-09T21:35:30","applicable-ai-for-businesses","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fscherm_afbeelding-2025-07-09-om-23.30.17.webp","https:\u002F\u002Feliovp.com\u002Fapplicable-ai-for-businesses\u002F",[1004,982,1100,1101,1199,1200,1201,1202,1106,1108,1034,1203,1204],{"path":1227,"title":1228,"description":1229,"date":1230,"slug":1231,"image":53,"originalUrl":1232,"categories":1233},"\u002Fblog\u002Fintroducing-paitons-free-evaluation-models","Introducing Paiton’s Free Evaluation Models","Test Paiton with free evaluation models for AMD GPUs. Compare text, vision and image generation performance using your own workloads.","2025-07-07T11:26:13","introducing-paitons-free-evaluation-models","https:\u002F\u002Feliovp.com\u002Fintroducing-paitons-free-evaluation-models\u002F",[1004,982,896],{"path":1235,"title":1236,"description":1237,"date":1238,"slug":1239,"image":1240,"originalUrl":1241,"categories":1242},"\u002Fblog\u002Fpaiton-dramatically-faster-startup-and-performance-for-llama-3-1-405b","Llama 3.1 405B: Faster Startup with Paiton on MI300X","See Paiton benchmarks for Llama 3.1 405B on eight AMD MI300X GPUs, covering model startup, tensor parallelism, throughput and latency.","2025-06-12T20:15:23","paiton-dramatically-faster-startup-and-performance-for-llama-3-1-405b","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fservingscreenshot.webp","https:\u002F\u002Feliovp.com\u002Fpaiton-dramatically-faster-startup-and-performance-for-llama-3-1-405b\u002F",[1004,982,896,1049,983,1154,1243,1156,1244,1245,1246,896,1247,1248],"Cold Start","Graph Compilation","Llama 3.1 405B","LLM Optimization","Startup Latency","Tensor Parallelism",{"path":1250,"title":1251,"description":1252,"date":1253,"slug":1254,"image":1255,"originalUrl":1256,"categories":1257},"\u002Fblog\u002Fpaiton-fp8-beats-nvidias-h200-on-amds-mi300x","Paiton FP8 Beats NVIDIA’s H200 on AMD’s MI300X","Compare Paiton on AMD MI300X with NVIDIA H200 for Llama 3.1 70B FP8, including throughput, first-token delay and latency across batch sizes.","2025-06-08T19:12:40","paiton-fp8-beats-nvidias-h200-on-amds-mi300x","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fblognewfp8.webp","https:\u002F\u002Feliovp.com\u002Fpaiton-fp8-beats-nvidias-h200-on-amds-mi300x\u002F",[1004,982,896,1049,1154,1258,1105,1025,984,985,986,1245,1259,1260],"Cold Start Optimization","Model Serving","vLLM Optimization",{"path":1262,"title":1263,"description":1264,"date":1265,"slug":1266,"image":1267,"originalUrl":1268,"categories":1269},"\u002Fblog\u002Fmi300x-vs-h200-vs-rx-7900-xtx-vs-tenstorrent-n300s-with-vllm","MI300X vs H200 vs RX 7900 XTX vs Tenstorrent n300s with vLLM","Compare MI300X, H200, RX 7900 XTX and Tenstorrent n300s on Llama 3 8B with vLLM, including throughput, modeled token costs and hardware limits.","2025-05-09T14:03:58","mi300x-vs-h200-vs-rx-7900-xtx-vs-tenstorrent-n300s-with-vllm","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fcomparisontenstor.webp","https:\u002F\u002Feliovp.com\u002Fmi300x-vs-h200-vs-rx-7900-xtx-vs-tenstorrent-n300s-with-vllm\u002F",[1004,982,896,1100,1049,1018,1216,1031,1270,1271],"RX7900XTX","tenstorrent",{"path":1273,"title":1274,"description":1275,"date":1276,"slug":1277,"image":1278,"originalUrl":1279,"categories":1280},"\u002Fblog\u002Fclusterpl-empowering-gpu-cluster-investors-with-real-world-financial-insights","ClusterP&L: Financial Modeling for GPU Clusters","Explore how ClusterP&L models GPU cluster costs, profitability and investment scenarios, with ROI metrics, risk simulations and exportable reports.","2025-05-03T10:52:22","clusterpl-empowering-gpu-cluster-investors-with-real-world-financial-insights","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fcomparisonscenarios.webp","https:\u002F\u002Feliovp.com\u002Fclusterpl-empowering-gpu-cluster-investors-with-real-world-financial-insights\u002F",[1004,982,862,1100,1019,1215,1281,1031,1282],"MI325x","pnl calculator",{"path":1284,"title":1285,"description":1286,"date":1287,"slug":1288,"image":1289,"originalUrl":1290,"categories":1291},"\u002Fblog\u002Fcranking-out-faster-tokens-for-fewer-dollars-amd-mi300x-vs-nvidia-h200","AMD MI300X vs. NVIDIA H200: Qwen3-32B with Paiton","Compare Qwen3-32B benchmarks on Paiton-optimized AMD MI300X and NVIDIA H200, covering throughput, latency and hardware costs.","2025-05-02T21:10:30","cranking-out-faster-tokens-for-fewer-dollars-amd-mi300x-vs-nvidia-h200","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002F3ac59a73-2466-4422-b7e5-ef2e4a8ca58e.webp","https:\u002F\u002Feliovp.com\u002Fcranking-out-faster-tokens-for-fewer-dollars-amd-mi300x-vs-nvidia-h200\u002F",[1004,982,896,1214,1018,1215,1292,1031,896,938],"MI300",{"path":1294,"title":1295,"description":1296,"date":1297,"slug":1298,"image":1299,"originalUrl":1300,"categories":1301},"\u002Fblog\u002Fpower-meets-precision-high-density-modular-data-center-for-nvidia-nvl-deployments-1-2-mw","Modular Data Centers for NVIDIA NVL: 1 to 2 MW","Explore modular data center designs for NVIDIA NVL systems, covering power capacity, liquid cooling, redundancy and deployment planning.","2025-05-02T14:09:59","power-meets-precision-high-density-modular-data-center-for-nvidia-nvl-deployments-1-2-mw","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Feliovp_critical-1mw-pod_rev-2_transparent.webp","https:\u002F\u002Feliovp.com\u002Fpower-meets-precision-high-density-modular-data-center-for-nvidia-nvl-deployments-1-2-mw\u002F",[1004,862,1302,863,1139,1006,867,1140,1303,1304,1143,1305],"1-2MW Data Center","NVIDIA Blackwell","NVIDIA NVL","Precision Cooling",{"path":1307,"title":1308,"description":1309,"date":1310,"slug":1311,"image":1312,"originalUrl":1313,"categories":1314},"\u002Fblog\u002Fexamining-ai-agents-in-the-medical-field-ai-that-speaks-dicom","Examining AI agents in the medical field: AI that speaks DICOM","Explore a local AI agent demo for DICOM workflows, from patient and study retrieval to a comparison of vision models using anonymized medical images.","2025-04-11T14:45:48","examining-ai-agents-in-the-medical-field-ai-that-speaks-dicom","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fhealthcareblog-1.webp","https:\u002F\u002Feliovp.com\u002Fexamining-ai-agents-in-the-medical-field-ai-that-speaks-dicom\u002F",[1004,982,1100,1049,1214,1018,1315],"Healthcare",{"path":1317,"title":1318,"description":1319,"date":1320,"slug":1321,"image":1322,"originalUrl":1323,"categories":1324},"\u002Fblog\u002Feliovp-bv-your-trusted-partner-for-supply-chain-resilience-amidst-new-u-s-tariffs","U.S. Tariffs and AI Supply Chain Resilience: April 2025","Read ElioVP's April 2025 perspective on U.S. tariffs and supply chain resilience for AI servers, HPC systems and modular data centers.","2025-04-04T10:01:27","eliovp-bv-your-trusted-partner-for-supply-chain-resilience-amidst-new-u-s-tariffs","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Ftariffsshipping.webp","https:\u002F\u002Feliovp.com\u002Feliovp-bv-your-trusted-partner-for-supply-chain-resilience-amidst-new-u-s-tariffs\u002F",[1004,1060,1214,1018,1325,1326,1327,1328],"import","Taiwan","Tariffs","Trump",{"path":1330,"title":1331,"description":1332,"date":1333,"slug":1334,"image":1335,"originalUrl":1336,"categories":1337},"\u002Fblog\u002Fwhy-ai-agents-are-the-future","Why AI Agents Are the Future","Explore AI agents for ERP, CRM, finance and customer support, with practical use cases and a path from workflow assessment to pilot and deployment.","2025-03-23T22:06:59","why-ai-agents-are-the-future","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Ferp2.jpeg","https:\u002F\u002Feliovp.com\u002Fwhy-ai-agents-are-the-future\u002F",[1004,982,1100,1214,1338,1339],"AI Agents","ERP",{"path":1341,"title":1342,"description":1343,"date":1344,"slug":1345,"image":1346,"originalUrl":1347,"categories":1348},"\u002Fblog\u002Fthe-rise-of-open-source-ai-model-optimization","The Rise of Open-Source AI Model Optimization","Explore open-source AI optimization trends, from quantization and mixture-of-experts models to hardware-aware tuning, RAG and edge deployment.","2025-03-22T20:59:23","the-rise-of-open-source-ai-model-optimization","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Friseofopensource.jpeg","https:\u002F\u002Feliovp.com\u002Fthe-rise-of-open-source-ai-model-optimization\u002F",[1004,982,1060,1349,1018,1026,1031],"AI news",{"path":1351,"title":1352,"description":1353,"date":1354,"slug":1355,"image":1356,"originalUrl":1357,"categories":1358},"\u002Fblog\u002Fintroducing-our-benchmarking-tool-powered-by-dstack","Introducing Our Benchmarking Tool: Powered by dstack","Explore our dstack-powered tool for reproducible vLLM benchmarks, automated parameter sweeps and performance reports across local and cloud GPUs.","2025-03-20T14:21:59","introducing-our-benchmarking-tool-powered-by-dstack","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fbenchmarktool.jpeg","https:\u002F\u002Feliovp.com\u002Fintroducing-our-benchmarking-tool-powered-by-dstack\u002F",[1004,982,896,1214,1018,1359,1360,1216,896],"benchmark","LLM",{"path":1362,"title":1363,"description":1364,"date":1365,"slug":1366,"image":1367,"originalUrl":1368,"categories":1369},"\u002Fblog\u002Foptimizing-qwq-32b-by-qwen-amd-mi300x-vs-nvidia-h200","Optimizing QwQ-32B (by Qwen): AMD MI300X vs. NVIDIA H200","Compare QwQ-32B throughput and latency on AMD MI300X with Paiton and NVIDIA H200, from small batches to higher concurrency.","2025-03-19T21:41:44","optimizing-qwq-32b-by-qwen-amd-mi300x-vs-nvidia-h200","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fpaiton4.jpeg","https:\u002F\u002Feliovp.com\u002Foptimizing-qwq-32b-by-qwen-amd-mi300x-vs-nvidia-h200\u002F",[1004,982,896],{"path":1371,"title":1372,"description":1373,"date":1374,"slug":1375,"image":1376,"originalUrl":1377,"categories":1378},"\u002Fblog\u002Feliovp-featured-on-amd-tech-talk-podcast","Eliovp Featured on AMD “Tech Talk” Podcast","Listen to Elio Van Puyvelde and Jim Greene on AMD's Tech Talk podcast, discussing ElioVP's origins and its AI hardware and software services.","2025-03-19T07:53:39","eliovp-featured-on-amd-tech-talk-podcast","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Ftechtalkjimgreene.jpeg","https:\u002F\u002Feliovp.com\u002Feliovp-featured-on-amd-tech-talk-podcast\u002F",[1004,1018,1379,1380,1381],"Jim Greene","Podcast","Tech Talk",{"path":1383,"title":1384,"description":1385,"date":1386,"slug":1387,"image":1388,"originalUrl":1389,"categories":1390},"\u002Fblog\u002Ffurther-optimizing-amd-powered-inference-with-paiton","Further Optimizing AMD-Powered Inference with Paiton","Explore Paiton's DeepSeek R1 Distill Llama 8B benchmarks on AMD MI300X, focusing on throughput and first-token latency at smaller batch sizes.","2025-03-13T06:18:30","further-optimizing-amd-powered-inference-with-paiton","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fpaitonpost3.webp","https:\u002F\u002Feliovp.com\u002Ffurther-optimizing-amd-powered-inference-with-paiton\u002F",[1004,982,896,1018,1391,1392,1215,1216,1281,896,938],"Deepseek","H100",{"path":1394,"title":1395,"description":1396,"date":1397,"slug":1398,"image":1399,"originalUrl":1400,"categories":1401},"\u002Fblog\u002Fa-first-look-at-paiton-in-action-deepseek-r1-distill-llama-3-1-8b","Paiton Benchmarks: DeepSeek R1 Distill Llama 3.1 8B","Compare stock and Paiton-optimized DeepSeek R1 Distill Llama 3.1 8B on AMD MI300X, with throughput and latency benchmarks across batch sizes.","2025-01-31T09:11:02","a-first-look-at-paiton-in-action-deepseek-r1-distill-llama-3-1-8b","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fpaitonpost2.webp","https:\u002F\u002Feliovp.com\u002Fa-first-look-at-paiton-in-action-deepseek-r1-distill-llama-3-1-8b\u002F",[1004,982,896,1018,1391,1392,1215,1216,1281,896,938],{"path":1403,"title":1404,"description":1405,"date":1406,"slug":1407,"image":1408,"originalUrl":1409,"categories":1410},"\u002Fblog\u002Fai-model-optimization-with-paiton","AI Model Optimization with Paiton","Learn how Paiton uses model compilation, custom kernels and kernel fusion to optimize AI inference on AMD GPUs.","2025-01-30T19:53:25","ai-model-optimization-with-paiton","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fpaitonpost1.webp","https:\u002F\u002Feliovp.com\u002Fai-model-optimization-with-paiton\u002F",[1004,982,896,1018,1392,1215,1216,1281,896,938],1792608180239]