[{"data":1,"prerenderedAt":1214},["ShallowReactive",2],{"blog-post-en-\u002Fblog\u002Fpaiton-minimax-h3-radeon-ai-pro-r9700":3,"blog-posts-sidebar-en":756},{"id":4,"title":5,"body":6,"categories":738,"date":744,"description":745,"extension":746,"heading":747,"image":748,"meta":749,"navigation":32,"originalUrl":750,"path":751,"seo":752,"slug":753,"stem":754,"__hash__":755},"blog\u002Fblog\u002Fpaiton-minimax-h3-radeon-ai-pro-r9700.md","MiniMax H3 on Radeon: 15-Second Video With Native Sound",{"type":7,"value":8,"toc":728},"minimark",[9,13,24,27,39,46,52,57,72,135,142,149,157,161,168,215,232,238,241,250,254,270,339,342,353,357,364,371,378,381,387,391,394,444,447,450,456,463,469,478,482,489,495,569,572,575,587,591,598,652,662,668,675,682,693,697,700,712,721,724],[10,11,12],"p",{},"A fox steps out of the trees and pauses beside a stream. The camera follows the scene, accompanied by birds and flowing water. This is one continuous MiniMax H3 generation, with its original stereo soundtrack, created locally on a single Radeon AI PRO R9700.",[10,14,15,19,20,23],{},[16,17,18],"strong",{},"Paiton produces the playable clip in 5 minutes 33 seconds on average."," Matched stock takes 6 minutes 39 seconds. That is about ",[16,21,22],{},"67 seconds less waiting per clip",", with identical MP4 hashes in the retained comparison.",[10,25,26],{},"This is the next free Paiton RDNA community package: local video with sound, an included ComfyUI workflow, and a benchmark that runs all the way from a fresh prompt to a saved MP4. No cloud inference service is required after setup.",[10,28,29],{},[30,31],"video",{"controls":32,"playsInline":32,"preload":33,"width":34,"height":35,"ariaLabel":36,"poster":37,"src":38},true,"none",864,480,"Generated fox scene with native stereo sound, 15 seconds","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-minimax-h3\u002Ffox-15s-poster.jpg","\u002Fasset\u002Fvideos\u002Fblog\u002Fpaiton-minimax-h3\u002Ffox-15s.mp4",[10,40,41,45],{},[42,43,44],"a",{"href":38},"Open the fox video (MP4, 5.5 MB)",". Press play to watch and hear the original sound.",[10,47,48],{},[49,50,51],"em",{},"One generated scene: 864 × 480, 362 frames at 24 fps, or 15.0833 seconds of video. Native 32 kHz stereo audio. No frame interpolation, upscaling, soundtrack replacement or clip concatenation. The 15 seconds describe the output, not the time needed to generate it.",[53,54,56],"h2",{"id":55},"the-same-clip-about-a-minute-sooner","The same clip, about a minute sooner",[10,58,59,60,63,64,67,68,71],{},"At the matched ",[16,61,62],{},"Turbo8 profile with eight denoiser evaluations",", Paiton reduces complete-request latency by ",[16,65,66],{},"16.66%",". At that generation rate, the same active runtime projects to ",[16,69,70],{},"19.99% more clips per hour",".",[73,74,75,92],"table",{},[76,77,78],"thead",{},[79,80,81,85,89],"tr",{},[82,83,84],"th",{},"Complete request, matched Turbo8",[82,86,88],{"align":87},"right","Stock",[82,90,91],{"align":87},"Paiton",[93,94,95,109,122],"tbody",{},[79,96,97,101,104],{},[98,99,100],"td",{},"Mean time to a saved, playable MP4",[98,102,103],{"align":87},"399.40 s",[98,105,106],{"align":87},[16,107,108],{},"332.85 s",[79,110,111,114,117],{},[98,112,113],{},"Approximate waiting time",[98,115,116],{"align":87},"6m 39s",[98,118,119],{"align":87},[16,120,121],{},"5m 33s",[79,123,124,127,130],{},[98,125,126],{},"Projected fixed-length clips per hour",[98,128,129],{"align":87},"9.01",[98,131,132],{"align":87},[16,133,134],{},"10.82",[10,136,137],{},[138,139],"img",{"alt":140,"src":141},"MiniMax H3 complete-request latency: 399.40 seconds for stock and 332.85 seconds for Paiton. Projected throughput rises from 9.01 to 10.82 clips per hour.","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-minimax-h3\u002Fgeneration-performance.webp",[10,143,144,145,148],{},"The comparison uses ",[16,146,147],{},"one fox prompt, seed 771, one warmup and two measured requests per engine",". Every measured request includes fresh conditioning, denoising, video and audio decoding, H.264\u002FAAC encoding, muxing and writing the MP4. Model downloads, process setup and the initial warmup are excluded.",[10,150,151,152,156],{},"The headline is therefore not a denoising-only speedup. It measures the wait for a playable file. Clips per hour is calculated as ",[153,154,155],"code",{},"3600 \u002F mean request seconds",", not measured in an hour-long production run. Prompt writing, review and rejected outputs add time in actual use.",[53,158,160],{"id":159},"start-creating-in-comfyui","Start creating in ComfyUI",[10,162,163,164,167],{},"On a Linux workstation with a ",[16,165,166],{},"32 GB Radeon AI PRO R9700",", Docker, Compose and working Radeon device access, run:",[169,170,175],"pre",{"className":171,"code":172,"language":173,"meta":174,"style":174},"language-bash shiki shiki-themes github-light github-dark","git clone --depth 1 https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin.git\ncd paiton-vllm-plugin\n.\u002Fmodels\u002FMiniMax-H3\u002Flaunch.sh\n","bash","",[153,176,177,200,209],{"__ignoreMap":174},[178,179,182,186,190,194,197],"span",{"class":180,"line":181},"line",1,[178,183,185],{"class":184},"sScJk","git",[178,187,189],{"class":188},"sZZnC"," clone",[178,191,193],{"class":192},"sj4cs"," --depth",[178,195,196],{"class":192}," 1",[178,198,199],{"class":188}," https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin.git\n",[178,201,203,206],{"class":180,"line":202},2,[178,204,205],{"class":192},"cd",[178,207,208],{"class":188}," paiton-vllm-plugin\n",[178,210,212],{"class":180,"line":211},3,[178,213,214],{"class":184},".\u002Fmodels\u002FMiniMax-H3\u002Flaunch.sh\n",[10,216,217,218,224,225,228,229,71],{},"Open ",[42,219,223],{"href":220,"rel":221},"http:\u002F\u002F127.0.0.1:8190\u002F?paiton=1",[222],"nofollow","ComfyUI on localhost",". The included workflow opens on the first visit. Edit the prompt, choose a seed and click ",[16,226,227],{},"Run",". The engine selector offers stock and Paiton, and videos are saved in ",[153,230,231],{},"~\u002Fpaiton-videos\u002F",[10,233,234],{},[138,235],{"alt":236,"src":237},"The supplied ComfyUI workflow, including the MiniMax H3 engine selector, Turbo8 settings, prompt, seed, and video and audio decoding.","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-minimax-h3\u002Fcomfyui-workflow.webp",[10,239,240],{},"The first launch downloads the artifact image and SHA-256-verified checkpoints, and installs pinned ComfyUI components locally. Later launches reuse the models, image and caches. Initial setup needs internet access; cached generation does not need a model API, paid inference service or cloud GPU.",[10,242,243,244,249],{},"The ",[42,245,248],{"href":246,"rel":247},"https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin\u002Ftree\u002Fmain\u002Fmodels\u002FMiniMax-H3",[222],"model guide"," contains the setup instructions, terminal commands, supported settings and license terms. Review the model and encoder licenses before use; the runtime does not replace those terms. The command above follows the repository's current default branch; use a recorded release or commit when reproducing a specific benchmark.",[53,251,253],{"id":252},"what-your-workstation-needs","What your workstation needs",[10,255,256,257,260,261,265,266,269],{},"This is a ",[16,258,259],{},"32 GB GPU workload",", not the lower-memory image-generation profile from our ",[42,262,264],{"href":263},"\u002Fblog\u002Fpaiton-flux2-klein-radeon-ai-pro-r9700","FLUX.2 klein release",". The long-clip record reaches ",[16,267,268],{},"30.34 GiB of sampled driver memory",". Components are scheduled with memory management between phases; the complete pipeline is not claimed to remain in VRAM at once.",[73,271,272,282],{},[76,273,274],{},[79,275,276,279],{},[82,277,278],{},"Requirement",[82,280,281],{},"Tested configuration or recommendation",[93,283,284,292,300,311,323,331],{},[79,285,286,289],{},[98,287,288],{},"GPU",[98,290,291],{},"One AMD Radeon AI PRO R9700, 32 GB",[79,293,294,297],{},[98,295,296],{},"Host",[98,298,299],{},"Intel i5-8400, 16 GB system RAM and 4 GB swap",[79,301,302,305],{},[98,303,304],{},"Recommended system RAM",[98,306,307,310],{},[16,308,309],{},"24 GB or more"," for the browser, longer requests and other applications",[79,312,313,316],{},[98,314,315],{},"Storage",[98,317,318,319,322],{},"SSD with ",[16,320,321],{},"60 GB free"," for the selected setup, caches and outputs",[79,324,325,328],{},[98,326,327],{},"Selected checkpoints",[98,329,330],{},"Approximately 33.96 GB; both adapters and their shared components total 35.92 GB",[79,332,333,336],{},[98,334,335],{},"Filesystem consideration",[98,337,338],{},"Without hard-link support, duplicate cache copies can require another 34 GB",[10,340,341],{},"The 16 GB host used swap. Across the retained records, lifetime process RSS reached 12.09 GiB and sampled process swap reached 1.24 GiB. Those figures are not evidence that every 16 GB system will run comfortably. Driver-memory telemetry was sampled every 0.5 seconds and may miss brief peaks.",[10,343,344,345,348,349,352],{},"With weights already present, the first 15-second requests took ",[16,346,347],{},"423.6 seconds for stock"," and ",[16,350,351],{},"381.0 seconds for Paiton",", after process setup. Downloads, verification, image preparation and cold caches add further time. The 332.85-second result is the mean of the two measured Paiton requests after that warmup, not first-launch time.",[53,354,356],{"id":355},"same-settings-same-retained-output","Same settings, same retained output",[10,358,359,360,363],{},"Both engines use the same upstream pruned and quantized ",[16,361,362],{},"W4A8 generation profile",", Turbo adapter, prompt, seed, frame count, scheduler and guidance. Paiton optimizes execution of that profile. It does not claim the upstream model pruning, quantization or Turbo training as its own work.",[10,365,366,367,370],{},"For the retained 15-second fox case, ",[16,368,369],{},"all six recorded MP4 outputs have the same SHA-256 hash",": a warmup and two measured requests from each engine. The complete results record and per-run CSV are included below. This supports exact output agreement for this test, not a promise of identical files for every model, prompt or future runtime.",[10,372,373,374,377],{},"The original full-precision 33B model is ",[16,375,376],{},"not"," the quality baseline. Both measured engines already include the upstream compression and adapter choices. MiniMax's hosted context-processing system and 2K regeneration stage are also outside this package. This is the local H3 base-generation path, not a claim to reproduce the entire hosted service.",[10,379,380],{},"The supplied quality review describes a consistent fox across the sequence, distinct decoded frames and natural sound. That is a useful retained example, not a comprehensive video-quality evaluation.",[10,382,383],{},[138,384],{"alt":385,"src":386},"Six retained frames from the fox clip at approximately 0, 3, 6, 9, 12 and 15 seconds.","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-minimax-h3\u002Ffox-15s-contact-sheet.jpg",[53,388,390],{"id":389},"four-steps-are-an-option-not-a-hidden-benchmark-shortcut","Four steps are an option, not a hidden benchmark shortcut",[10,392,393],{},"An earlier short-clip suite covered wildlife, a speaking barista and pouring water. These outputs contain 124 frames at 24 fps, or approximately 5.17 seconds of video, with native stereo audio.",[73,395,396,410],{},[76,397,398],{},[79,399,400,403,405,407],{},[82,401,402],{},"Earlier short-clip suite",[82,404,88],{"align":87},[82,406,91],{"align":87},[82,408,409],{"align":87},"Lower latency",[93,411,412,428],{},[79,413,414,417,420,425],{},[98,415,416],{},"Turbo8, eight evaluations",[98,418,419],{"align":87},"95.83 s",[98,421,422],{"align":87},[16,423,424],{},"80.60 s",[98,426,427],{"align":87},"15.89%",[79,429,430,433,436,441],{},[98,431,432],{},"Turbo4, four evaluations",[98,434,435],{"align":87},"64.15 s",[98,437,438],{"align":87},[16,439,440],{},"54.38 s",[98,442,443],{"align":87},"15.24%",[10,445,446],{},"These are separate short-clip results reported in the release notes, not additional samples in the 15-second benchmark. Each row compares the same adapter and evaluation count across engines.",[10,448,449],{},"The four-step adapter gives a faster iteration option, but it changes the quality tradeoff. In the pouring test, it produced two bottles where one was requested. Eight steps preserved one bottle but still showed excessive foam and incomplete placement. Both versions had a brief native audio transient.",[10,451,452,455],{},[16,453,454],{},"Turbo8 remains the quality-oriented default."," We do not compare four-step Paiton against eight-step stock and call the reduced work a compiler speedup.",[10,457,458],{},[30,459],{"controls":32,"playsInline":32,"preload":33,"width":34,"height":35,"ariaLabel":460,"poster":461,"src":462},"Generated barista dialogue with native sound, 5 seconds","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-minimax-h3\u002Fbarista-5s-poster.jpg","\u002Fasset\u002Fvideos\u002Fblog\u002Fpaiton-minimax-h3\u002Fbarista-5s.mp4",[10,464,465,468],{},[42,466,467],{"href":462},"Open the barista video (MP4, 0.7 MB)",". Spoken line: “Here is your coffee.”",[10,470,471],{},[49,472,473,474,71],{},"Turbo4 dialogue example, approximately 5.17 seconds. The supplied reviewer notes report intelligible “Here is your coffee” speech and reasonable lip synchronization on the byte-identical stock counterpart. This is a separate example from the timed 15-second fox comparison. ",[42,475,477],{"href":476},"\u002Fdownloads\u002Fpaiton-minimax-h3\u002Fbarista-provenance.json","Clip provenance",[53,479,481],{"id":480},"measure-the-whole-request","Measure the whole request",[10,483,484,485,488],{},"The benchmark ran on the workstation's existing ",[16,486,487],{},"AUTO\u002FCOMPUTE profile",". We did not alter clock, power, voltage or cooling controls. Conditioning is recomputed for each request rather than reused from a previous prompt.",[10,490,491],{},[138,492],{"alt":493,"src":494},"Mean time spent on conditioning, denoising, video decoding, audio decoding, and encoding and muxing for stock and Paiton.","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-minimax-h3\u002Fcomponent-times.webp",[73,496,497,508],{},[76,498,499],{},[79,500,501,504,506],{},[82,502,503],{},"Mean time per request phase",[82,505,88],{"align":87},[82,507,91],{"align":87},[93,509,510,523,536,547,558],{},[79,511,512,515,518],{},[98,513,514],{},"Conditioning",[98,516,517],{"align":87},"21.72 s",[98,519,520],{"align":87},[16,521,522],{},"10.82 s",[79,524,525,528,531],{},[98,526,527],{},"Denoising",[98,529,530],{"align":87},"317.99 s",[98,532,533],{"align":87},[16,534,535],{},"262.30 s",[79,537,538,541,544],{},[98,539,540],{},"Video decoding",[98,542,543],{"align":87},"42.52 s",[98,545,546],{"align":87},"42.47 s",[79,548,549,552,555],{},[98,550,551],{},"Audio decoding",[98,553,554],{"align":87},"3.35 s",[98,556,557],{"align":87},"3.37 s",[79,559,560,563,566],{},[98,561,562],{},"Encoding and muxing",[98,564,565],{"align":87},"13.80 s",[98,567,568],{"align":87},"13.87 s",[10,570,571],{},"The phase measurements account for essentially the complete request. Small bookkeeping intervals sit outside those phases. Keeping the saved-file boundary matters: accelerating model execution does not make video decoding, audio decoding or file encoding disappear.",[10,573,574],{},"This is a small, fixed-seed comparison on one workstation. It establishes the result for the published profile, not a speed guarantee for all prompts or a claim to be faster than every other H3 runtime.",[10,576,577,578,348,582,586],{},"The downloadable ",[42,579,581],{"href":580},"\u002Fdownloads\u002Fpaiton-minimax-h3\u002Flong15-timings.csv","per-run timings",[42,583,585],{"href":584},"\u002Fdownloads\u002Fpaiton-minimax-h3\u002Fbenchmark-results.json","benchmark results"," preserve the measured values, settings, output hashes and memory summaries. Paiton's compiler implementation remains separate from this public benchmark evidence.",[53,588,590],{"id":589},"lower-cost-per-generated-minute","Lower cost per generated minute",[10,592,593,594,597],{},"A video workload calls for video economics. We count ",[16,595,596],{},"cost per complete clip and per generated minute",", not internal diffusion tokens.",[73,599,600,611],{},[76,601,602],{},[79,603,604,607,609],{},[82,605,606],{},"Illustrative ownership cost",[82,608,88],{"align":87},[82,610,91],{"align":87},[93,612,613,626,639],{},[79,614,615,618,621],{},[98,616,617],{},"USD per 15.08-second clip",[98,619,620],{"align":87},"$0.0373",[98,622,623],{"align":87},[16,624,625],{},"$0.0311",[79,627,628,631,634],{},[98,629,630],{},"USD per generated video minute",[98,632,633],{"align":87},"$0.148",[98,635,636],{"align":87},[16,637,638],{},"$0.124",[79,640,641,644,647],{},[98,642,643],{},"EUR per generated video minute",[98,645,646],{"align":87},"€0.205",[98,648,649],{"align":87},[16,650,651],{},"€0.171",[10,653,654,655,658,659,71],{},"At equal assumed cost per productive hour, the measured latency reduction translates into ",[16,656,657],{},"16.66% lower modeled cost per generated minute",". The inverse is ",[16,660,661],{},"19.99% more generated video for the same active-runtime budget",[10,663,664],{},[138,665],{"alt":666,"src":667},"Modeled cost per generated video minute falls by 16.7% at equal hourly cost. The US scenario moves from $0.148 to $0.124; the European scenario from €0.205 to €0.171.","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-minimax-h3\u002Fmodeled-cost.webp",[10,669,670,671,674],{},"The scenarios amortize a $1,299 GPU with $0.17\u002FkWh electricity, or a €1,749 GPU with €0.2558\u002FkWh electricity, over 5,000 productive hours. Both engines are assigned an equal ",[16,672,673],{},"450 W whole-system draw",". These are scenario inputs, not current retail quotes or measured wall power.",[10,676,677,678,681],{},"The calculation excludes the host purchase, idle time, cooling, maintenance, financing, tax, residual value, manual review and rejected generations. ",[16,679,680],{},"If only half of the generated clips are usable, cost per accepted clip doubles."," These are generation costs, not the price of a finished production minute.",[10,683,243,684,348,688,692],{},[42,685,687],{"href":686},"\u002Fdownloads\u002Fpaiton-minimax-h3\u002Feconomics.json","scenario data",[42,689,691],{"href":690},"\u002Fdownloads\u002Fpaiton-minimax-h3\u002Feconomics.py","calculator"," are included so you can substitute your own hardware cost, utilization and electricity rate.",[53,694,696],{"id":695},"try-the-local-package-bring-us-the-production-workload","Try the local package. Bring us the production workload.",[10,698,699],{},"This release adds video with native sound to Paiton's free RDNA community packages, alongside local language models and image generation. The immediate result is practical: the same retained clip, on the same Radeon, saved about a minute sooner.",[10,701,702,706,707,71],{},[42,703,705],{"href":246,"rel":704},[222],"Get the MiniMax H3 setup and benchmarks",", or inspect the ",[42,708,711],{"href":709,"rel":710},"https:\u002F\u002Fhuggingface.co\u002FEliovpAI\u002FMiniMax-H3-W4A8-Paiton-RDNA4",[222],"Hugging Face runtime package",[10,713,714,717,718,71],{},[16,715,716],{},"Running production inference on AMD Instinct or CDNA?"," Talk to us about your models, latency targets and cost per usable result. This Radeon benchmark is one qualified workload; larger deployments need their own measurements. Learn more about ",[42,719,91],{"href":720},"\u002Fproducts\u002Fpaiton",[10,722,723],{},"Powered by MiniMax H3.",[725,726,727],"style",{},"html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":174,"searchDepth":202,"depth":202,"links":729},[730,731,732,733,734,735,736,737],{"id":55,"depth":202,"text":56},{"id":159,"depth":202,"text":160},{"id":252,"depth":202,"text":253},{"id":355,"depth":202,"text":356},{"id":389,"depth":202,"text":390},{"id":480,"depth":202,"text":481},{"id":589,"depth":202,"text":590},{"id":695,"depth":202,"text":696},[91,739,740,741,742,743],"AMD Radeon","Local AI","Video Generation","MiniMax H3","ComfyUI","2026-09-09T07:30:00Z","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.","md","15 seconds of video. Native sound. One Radeon.","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-minimax-h3\u002F00-featured-minimax-h3-r9700.webp",{},"https:\u002F\u002Feliovp.com\u002Fblog\u002Fpaiton-minimax-h3-radeon-ai-pro-r9700","\u002Fblog\u002Fpaiton-minimax-h3-radeon-ai-pro-r9700",{"title":5,"description":745},"paiton-minimax-h3-radeon-ai-pro-r9700","blog\u002Fpaiton-minimax-h3-radeon-ai-pro-r9700","zGaVuQEVi1cvr4TASZLkGmVhsu4RrbedRbPNpmXtKdI",[757,759,768,785,794,809,840,852,875,893,912,930,948,965,977,993,1008,1020,1029,1037,1052,1064,1075,1086,1096,1109,1119,1132,1143,1153,1164,1173,1185,1196,1205],{"path":751,"title":5,"description":745,"date":744,"slug":753,"image":748,"originalUrl":750,"categories":758},[91,739,740,741,742,743],{"path":263,"title":760,"description":761,"date":762,"slug":763,"image":764,"originalUrl":263,"categories":765},"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",[91,739,740,766,767,743],"Image Generation","FLUX",{"path":769,"title":770,"description":771,"date":772,"slug":773,"image":774,"originalUrl":775,"categories":776},"\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",[91,777,739,778,779,780,781,782,783,784],"Artificial Intelligence","AI Inference","GPU Performance","Inference Latency","Inference Optimization","Large Language Models","vLLM","Cost Efficiency",{"path":786,"title":787,"description":788,"date":789,"slug":790,"image":791,"originalUrl":792,"categories":793},"\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",[91,777,739,778,779,780,781,782,783,784],{"path":795,"title":796,"description":797,"date":798,"slug":799,"image":800,"originalUrl":801,"categories":802},"\u002Fblog\u002Fai-data-center-power-requirements-gpu-per-megawatt","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",null,[803,804,805,806,807,808],"All","AI Infrastructure","Data Centers","ModFlex","HPC","AMD Helios",{"path":810,"title":811,"description":812,"date":813,"slug":814,"image":815,"originalUrl":816,"categories":817},"\u002Fblog\u002Fpaiton-returns-to-its-diffusion-roots-optimizing-wan2-2-t2v-a14b-on-amd-mi355x","Paiton Returns to Its Diffusion Roots: Optimizing Wan2.2-T2V-A14B on AMD MI355X","When we first started building Paiton, one of our earliest focus areas was optimizing diffusion models. Stable Diffusion XL was one of the first large models where we showed that fused operators, efficient execution, and hardware-aware kernels could make a real difference. Now we are returning to those origins.With the growing interest in text-to-video generation, ...","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",[803,777,91,818,819,820,821,822,823,824,825,826,288,827,828,829,830,831,832,833,91,834,835,836,837,838,839],"14B","AMD","B200","Benchmarks","Blackwell","Compute","Diffusion","Eliovp","Generative AI","Hardware","Inference","Instinct","MI355x","NVidia","On-Premise","Optimization","Sovereign AI","T2V","Text-to-Video","Tuning","Video-Generation","Wan2.2",{"path":841,"title":842,"description":843,"date":844,"slug":845,"image":846,"originalUrl":847,"categories":848},"\u002Fblog\u002Ffrom-the-attic-to-the-front-page-eliovp-recognized-as-a-pioneer-in-chip-optimization-data-center-infrastructure","From the Attic to the Front Page: ElioVP Recognized as a Pioneer in Chip Optimization & Data Center Infrastructure","It has been some incredible weeks for the team here at Eliovp. We are extremely proud to share that our company was recently featured on the front page of De Tijd, Belgium’s leading business newspaper. Seeing our story, from our founder’s early days tinkering with wires in an attic to generating €215 million in revenue, ...","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",[803,777,849,850,819,851,849,831],"Modular DC","Uncategorized","De Tijd",{"path":853,"title":854,"description":855,"date":856,"slug":857,"image":858,"originalUrl":859,"categories":860},"\u002Fblog\u002Fprivacy-is-geen-it-probleem-meer-het-is-een-strategische-prioriteit","Privacy is geen IT-probleem meer, het is een strategische prioriteit: ","Privacyrisico’s, Psychologische Valkuilen en de Operationele Realiteit van Generatieve AI in de Benelux De recente verschijning van een frontpage-artikel over ons bedrijf in het gerespecteerde dagblad “De Tijd” heeft onze zichtbaarheid aanzienlijk vergroot, wat de aanleiding is voor dit artikel. Deze mediabelangstelling, gecombineerd met de talrijke uitnodigingen voor spreekbeurten die we hebben ontvangen, fungeert als ...","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",[803,777,861,850,862,863,864,865,866,867,868,869,825,870,871,872,873,874],"Trending","AI Act","Antropomorfisme","AVG","Benelux","ChatGPT","Cybersecurity","Data Governance","Data Security","GDPR","Generatieve AI","Microsoft Copilot","Privacy","Shadow AI",{"path":876,"title":877,"description":878,"date":879,"slug":880,"image":881,"originalUrl":882,"categories":883},"\u002Fblog\u002Fitsme-bij-ons-is-het-its-not-me-en-dit-is-waarom","Itsme? Bij ons is het “it’s not me”, en dit is waarom.","1. Inleiding: De Strategische Noodzaak van Weigering In het hedendaagse digitale landschap wordt de keuze voor een identiteit leverancier (IdP) vaak gereduceerd tot een discussie over User Experience (UX) en conversieratio’s. Deze reductionistische benadering verhult echter de diepgaande strategische, juridische en operationele risico’s die gepaard gaan met het uitbesteden van de “Sleutels tot het Koninkrijk”, ...","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",[803,884,885,886,867,887,888,889,870,890,891,892,873],"AWS","Belgian Mobile ID","Cloud Act","Data Soevereiniteit","Digitale Identiteit","eIDAS","itsme","Liberty Global","MyGov.be",{"path":894,"title":895,"description":896,"date":897,"slug":898,"image":899,"originalUrl":900,"categories":901},"\u002Fblog\u002Ffield-report-the-reality-of-building-agentic-ai-in-2025","Field Report. The Reality of Building Agentic AI in 2025","From Hype to Sovereign Infrastructure Nederlandse Versie Summary The narrative surrounding “Agentic AI” in 2025 is defined by a sharp contrast between market expectations and engineering reality. While the general public, conditioned by the ease of ChatGPT, expects “miracles” and instant integration, the reality of building autonomous agents for enterprise workflows is a discipline of ...","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",[803,777,902,861,903,904,905,906,907,908,909,910,834,911],"Solutions","Agentic AI","AI Engineering","AI Strategy","Autonomous Agents","Enterprise AI","Local LLM","Model Fine-Tuning","On-Premise AI","VRAM Optimization",{"path":913,"title":914,"description":915,"date":916,"slug":917,"image":918,"originalUrl":919,"categories":920},"\u002Fblog\u002Fthe-synthetic-unicorn-bubble","The Synthetic Unicorn Bubble","How AI Neocloud Infrastructure Turns Circular Cash Flows Into Fake Growth Executive Summary The interval between 2023 and 2025 has birthed a capital allocation phenomenon arguably without precedent: the “Synthetic Bubble.” Driven by the scramble for AI dominance, the venture capital apparatus has directed billions into the “Neocloud” ecosystem. However, a rigorous analysis suggests that ...","2025-11-24T19:22:28","the-synthetic-unicorn-bubble","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fsyntheticunicorn.webp","https:\u002F\u002Feliovp.com\u002Fthe-synthetic-unicorn-bubble\u002F",[803,777,861,804,921,922,923,924,925,926,927,928,929],"AI Neocloud","Circular Financing","GPU Cloud","Investment Risks","Startup Valuation","Synthetic Bubble","Tech Analysis","Vaporware","Venture Capital",{"path":931,"title":932,"description":933,"date":934,"slug":935,"image":936,"originalUrl":937,"categories":938},"\u002Fblog\u002Fbuilding-the-engine-for-the-ai-race-the-4-month-path-to-nvidia-gb300-nvl72-power","Building the Engine for the AI Race: The 4-Month Path to NVIDIA GB300 NVL72 Power","In artificial intelligence infrastructure, speed is the foundation of competitive differentiation. From model training velocity to inference latency, every millisecond matters. But before any workload executes, there is a critical prerequisite that often determines success or failure: time to market. Traditional builds typically require 18–24 months. In the current AI cycle, that is simply too ...","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",[803,849,850,939,804,940,941,942,943,944,945,946,947],"150kW Rack","DLC","High Density","Liquid Cooling","Modular Data Center","NVIDIA Blackwell Ultra","NVIDIA GB300","NVL72","Rapid Deployment",{"path":949,"title":950,"description":951,"date":952,"slug":953,"image":954,"originalUrl":955,"categories":956},"\u002Fblog\u002Fwhy-cuda-translation-wont-unlock-amds-real-potential","Why “CUDA” Translation Won’t Unlock AMD’s Real Potential","Every few years, a new solution pops up promising the same dream: On paper, that sounds perfect. Take your existing CUDA applications, swap out the toolchain, and suddenly you’re “portable.” And to be fair: if you’re running research code or trying to get an internal tool to compile on a non-NVIDIA box, that can absolutely ...","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",[803,777,91,850,957,777,958,959,960,961,962,963,91,964],"AMD MI300X","CUDA Translation","FP8","GPU Optimization","High Performance Computing","HIP","Kernel Tuning","ROCm",{"path":966,"title":967,"description":968,"date":969,"slug":970,"image":971,"originalUrl":972,"categories":973},"\u002Fblog\u002Fpaiton-the-simplest-way-to-supercharge-ai-inference","Paiton: The Simplest Way to Supercharge AI Inference","Let’s be honest, we’re not the marketing type.We’ve never taken a cent of outside investment, never burned cash on ad campaigns, and never hired a sales army.We just build things that work. In today’s world, it seems the companies shouting the loudest often get the spotlight, while the ones doing the actual engineering quietly build ...","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",[803,777,91,778,974,957,784,975,781,963,91,976,783],"AMD Instinct","High Throughput","SGLang",{"path":978,"title":979,"description":980,"date":981,"slug":982,"image":983,"originalUrl":984,"categories":985},"\u002Fblog\u002Fstop-overpaying-paiton-mi300x-moe-beats-h200-b200-on-1m-tokens","Stop Overpaying: Paiton MI300X MoE Beats H200\u002FB200 on $\u002F1M Tokens","Short summary: We benchmarked Paiton with our new MoE support on Qwen\u002FQwen3-30B-A3B-Instruct-2507 to compare inference performance across several setups. Each configuration was run five times per batch size and we report the mean across runs. Why this benchmark Most published numbers use synthetic prompts or toy datasets. We focused on realistic conversational workloads (we always ...","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",[803,777,91,986,957,987,781,988,989,990,991,91,992],"AI Benchmarks","Cost per Token","Mixture of Experts","MoE","NVIDIA B200","NVIDIA H200","Qwen3",{"path":994,"title":995,"description":996,"date":997,"slug":998,"image":999,"originalUrl":1000,"categories":1001},"\u002Fblog\u002Fagentic-ai-but-make-it-local-from-inbox-to-insight-to-action-en","Agentic AI, But Make It Local: From Inbox to Insight to Action","(Nederlandse versie) We’ve built production-ready, local-first agentic AI that plugs into your existing email stack, auto-creates tickets, classifies messages, extracts multi-question threads, reads PDFs, spots invoices\u002Fquotes, analyzes images (yes, damage detection), and pushes structured reports into your systems, no dependency on OpenAI, Google, or Microsoft unless you want it. Tailor-made models trained on your data, ...","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",[803,777,902,850,903,1002,1003,1004,1005,908,910,834,1006,1007],"Damage Detection","Document Processing","Email Automation","Invoice Extraction","Ticket Automation","Workflow Automation",{"path":1009,"title":1010,"description":1011,"date":1012,"slug":1013,"image":1014,"originalUrl":1015,"categories":1016},"\u002Fblog\u002Fmi300x-fp8-data-parallel-benchmarks-8-64-gpus-h200-left-behind-b200-within-reach","MI300X FP8 Data‑Parallel Benchmarks (8–64 GPUs): H200 Left Behind, B200 Within Reach","At ElioVP, we’re all about pushing AI inference past the limits, and packaging every squeeze of performance into a plug‑and‑play runtime.  Remember our last blog, where Paiton’s FP8 pipeline on AMD’s MI300X completely outclassed NVIDIA’s H200? Well, buckle up, because we’ve gone back to the drawing board. This time, we’re loading Llama-3.1-8B-Instruct-FP8-KV, the leaner, meaner ...","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",[803,777,91,850,1017,819,820,1018,1019,830,831,91,783],"AI","H200","MI300X",{"path":1021,"title":1022,"description":1023,"date":1024,"slug":1025,"image":1026,"originalUrl":1027,"categories":1028},"\u002Fblog\u002Fapplicable-ai-for-businesses","Applicable AI for Businesses","Here at Eliovp, we continuously innovate when it comes to building practical solutions. If there’s one core strength, it’s our team’s ability to think outside the box. One key area of focus for us is developing applicable AI solutions, everyday usable AI implementations tailored specifically for our clients’ needs. In this blog, we’ll explore our ...","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",[803,777,902,903,1002,1003,1004,1005,908,910,834,1006,1007],{"path":1030,"title":1031,"description":1032,"date":1033,"slug":1034,"image":174,"originalUrl":1035,"categories":1036},"\u002Fblog\u002Fintroducing-paitons-free-evaluation-models","Introducing Paiton’s Free Evaluation Models","Introduction AI is rapidly transforming every industry, but running large models efficiently remains a major technical and financial challenge. At ElioVP, we specialize in optimizing for AMD accelerators, helping organizations unlock the full potential of their hardware. Today, we’re excited to announce a new offering: free evaluation models that let you test our cutting-edge optimizations ...","2025-07-07T11:26:13","introducing-paitons-free-evaluation-models","https:\u002F\u002Feliovp.com\u002Fintroducing-paitons-free-evaluation-models\u002F",[803,777,91],{"path":1038,"title":1039,"description":1040,"date":1041,"slug":1042,"image":1043,"originalUrl":1044,"categories":1045},"\u002Fblog\u002Fpaiton-dramatically-faster-startup-and-performance-for-llama-3-1-405b","Paiton: Dramatically Faster Startup and Performance for Llama-3.1-405B","With Paiton, we’re not merely pursuing peak inference speeds, we’re fundamentally reshaping the entire lifecycle of large language model (LLM) deployment. Our latest endeavor pairs AMD’s cutting-edge MI300X GPUs with the colossal Llama-3.1-405B-Instruct-FP8-KV model, achieving groundbreaking milestones: Visual Demonstration: Startup Speed Showcase We’re excited to share a visual demonstration of Paiton’s revolutionary startup performance. Watch ...","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",[803,777,91,850,778,957,1046,959,1047,1048,1049,91,1050,1051],"Cold Start","Graph Compilation","Llama 3.1 405B","LLM Optimization","Startup Latency","Tensor Parallelism",{"path":1053,"title":1054,"description":1055,"date":1056,"slug":1057,"image":1058,"originalUrl":1059,"categories":1060},"\u002Fblog\u002Fpaiton-fp8-beats-nvidias-h200-on-amds-mi300x","Paiton FP8 Beats NVIDIA’s H200 on AMD’s MI300X","The world of AI is moving at an unprecedented pace, and efficient inference is key to deploying powerful models in real-world applications. At Eliovp, we’ve consistently pushed the boundaries of AI performance, as highlighted in our previous blogs showcasing significant inference speedups when benchmarking with fp16\u002Fbf16. Now, we’re thrilled to announce a further significant leap ...","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",[803,777,91,850,957,1061,907,826,779,780,782,1048,1062,1063],"Cold Start Optimization","Model Serving","vLLM Optimization",{"path":1065,"title":1066,"description":1067,"date":1068,"slug":1069,"image":1070,"originalUrl":1071,"categories":1072},"\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","As large language models (LLMs) become a foundational part of modern applications, picking the right server for deployment is more important than ever. Whether you’re an enterprise scaling up inference, a startup optimizing for cost, or a researcher pushing throughput boundaries. This blog compares two high-profile server setups and two not so high-profile setups which ...","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",[803,777,91,902,850,819,1019,831,1073,1074],"RX7900XTX","tenstorrent",{"path":1076,"title":1077,"description":1078,"date":1079,"slug":1080,"image":1081,"originalUrl":1082,"categories":1083},"\u002Fblog\u002Fclusterpl-empowering-gpu-cluster-investors-with-real-world-financial-insights","ClusterP&L: Empowering GPU Cluster Investors with Real-World Financial Insights","At Eliovp BV, we’ve spent years on the cutting edge of GPU cluster deployment and optimization across Europe. Our team supports leading organizations in AI, finance, and research, architecting, building, and scaling high-performance infrastructure. Over time, our customers, both newcomers and seasoned adopters, repeatedly asked the same question: “Can you help us build a P&L ...","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",[803,777,849,902,820,1018,1084,831,1085],"MI325x","pnl calculator",{"path":1087,"title":1088,"description":1089,"date":1090,"slug":1091,"image":1092,"originalUrl":1093,"categories":1094},"\u002Fblog\u002Fcranking-out-faster-tokens-for-fewer-dollars-amd-mi300x-vs-nvidia-h200","Cranking Out Faster Tokens for Fewer Dollars: AMD MI300X vs. NVIDIA H200","Qwen3-32B on Paiton + AMD MI300x vs.NVIDIA H200 1. Introduction “While we’re actively training models for local customers, automating and streamlining critical business processes, we still found time to push our Paiton framework to the limit on Qwen3-32B.” In the competitive realm of LLMs, next-gen hardware like the NVIDIA H200 often steals the headlines. But ...","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",[803,777,91,1017,819,1018,1095,831,91,783],"MI300",{"path":1097,"title":1098,"description":1099,"date":1100,"slug":1101,"image":1102,"originalUrl":1103,"categories":1104},"\u002Fblog\u002Fpower-meets-precision-high-density-modular-data-center-for-nvidia-nvl-deployments-1-2-mw","Power Meets Precision: High-Density Modular Data Center for NVIDIA NVL Deployments (1–2 MW)","Purpose-Built High-Density Infrastructure for Blackwell-Class AI Workloads At Eliovp, we’re engineering a new class of AI infrastructure. Our advanced modular platform is built to also support NVIDIA’s cutting-edge NVL architecture, from the efficient NVL4 to the ultra-scale NVL72, enabling deployments that range from distributed edge inference to full-stack model training at hyperscale. Designed to meet ...","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",[803,849,1105,804,941,807,942,943,1106,1107,946,1108],"1-2MW Data Center","NVIDIA Blackwell","NVIDIA NVL","Precision Cooling",{"path":1110,"title":1111,"description":1112,"date":1113,"slug":1114,"image":1115,"originalUrl":1116,"categories":1117},"\u002Fblog\u002Fexamining-ai-agents-in-the-medical-field-ai-that-speaks-dicom","Examining AI agents in the medical field: AI that speaks DICOM","At Eliovp, we’re constantly keeping up with the newest AI trends. Consequently, we have been looking into AI agents and have created a medical agent designed to seamlessly interact with DICOM servers inside hospitals. This isn’t just another chatbot or AI tool. This is an intelligent assistant that understands the language of radiology and is ...","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",[803,777,902,850,1017,819,1118],"Healthcare",{"path":1120,"title":1121,"description":1122,"date":1123,"slug":1124,"image":1125,"originalUrl":1126,"categories":1127},"\u002Fblog\u002Feliovp-bv-your-trusted-partner-for-supply-chain-resilience-amidst-new-u-s-tariffs","Eliovp BV: Your Trusted Partner for Supply Chain Resilience Amidst New U.S. Tariffs","In today’s rapidly evolving global trade landscape, businesses face unprecedented challenges in maintaining efficient and cost-effective IT infrastructure. The recent U.S. tariff adjustments have created waves of uncertainty across international markets, particularly for companies relying on high-performance computing and AI solutions. At Eliovp BV, we want to assure our valued clients that our comprehensive end-to-end ...","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",[803,861,1017,819,1128,1129,1130,1131],"import","Taiwan","Tariffs","Trump",{"path":1133,"title":1134,"description":1135,"date":1136,"slug":1137,"image":1138,"originalUrl":1139,"categories":1140},"\u002Fblog\u002Fwhy-ai-agents-are-the-future","Why AI Agents Are the Future","1. Versatile IntegrationAI Agents are designed to integrate seamlessly with your existing software stack. This includes ERP, CRM, and marketing automation platforms. Instead of disrupting current systems, they complement and enhance them, all while learning from and adapting to your specific operational needs. 2. Intelligent Decision-MakingConventional automation scripts handle if-then scenarios, but they fall short ...","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",[803,777,902,1017,1141,1142],"AI Agents","ERP",{"path":1144,"title":1145,"description":1146,"date":1147,"slug":1148,"image":1149,"originalUrl":1150,"categories":1151},"\u002Fblog\u002Fthe-rise-of-open-source-ai-model-optimization","The Rise of Open-Source AI Model Optimization","In the rapidly evolving landscape of artificial intelligence (AI), open-source solutions are emerging as pivotal drivers of innovation and performance enhancement. These community-driven platforms democratize access to cutting-edge technologies, fostering collaboration and accelerating advancements in AI model optimization. The Open-Source Revolution in AI Open-source AI models have transformed the development and deployment of machine learning ...","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",[803,777,861,1152,819,288,831],"AI news",{"path":1154,"title":1155,"description":1156,"date":1157,"slug":1158,"image":1159,"originalUrl":1160,"categories":1161},"\u002Fblog\u002Fintroducing-our-benchmarking-tool-powered-by-dstack","Introducing Our Benchmarking Tool: Powered by dstack","1. Introduction Benchmarking is an essential part of optimizing AI models and software applications. Whether you’re testing AI model inference speeds, profiling different hardware configurations, or ensuring system performance over time, having a reliable benchmarking tool is crucial. However, many existing tools suffer from issues like inconsistent environments, difficult configuration setups, and lack of automation. ...","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",[803,777,91,1017,819,1162,1163,1019,91],"benchmark","LLM",{"path":1165,"title":1166,"description":1167,"date":1168,"slug":1169,"image":1170,"originalUrl":1171,"categories":1172},"\u002Fblog\u002Foptimizing-qwq-32b-by-qwen-amd-mi300x-vs-nvidia-h200","Optimizing QwQ-32B (by Qwen): AMD MI300X vs. NVIDIA H200","1. Introduction In the world of large language models (LLMs), most benchmarks center on Llama or DeepSeek derivatives. We decided to diversify by adding the Qwen2 architecture, using our Paiton framework. This 32-billion-parameter model pushes GPU resources to the limit, perfect for comparing NVIDIA’s new H200 to our AMD MI300X, which leverages Paiton for advanced ...","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",[803,777,91],{"path":1174,"title":1175,"description":1176,"date":1177,"slug":1178,"image":1179,"originalUrl":1180,"categories":1181},"\u002Fblog\u002Feliovp-featured-on-amd-tech-talk-podcast","Eliovp Featured on AMD “Tech Talk” Podcast","We’re excited to share that Eliovp was recently featured on AMD’s “Tech Talk” podcast! In this episode, our CEO, Elio Van Puyvelde sits down with Jim greene to talk about the origins of Eliovp, the passion and expertise that brought the company to life, and the innovative full end-to-end solutions we offer today. From our ...","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",[803,819,1182,1183,1184],"Jim Greene","Podcast","Tech Talk",{"path":1186,"title":1187,"description":1188,"date":1189,"slug":1190,"image":1191,"originalUrl":1192,"categories":1193},"\u002Fblog\u002Ffurther-optimizing-amd-powered-inference-with-paiton","Further Optimizing AMD-Powered Inference with Paiton","Executive Summary If you’ve followed our journey so far, you’ll know that Paiton is laser-focused on AMD-centric inference optimization. Our latest work takes DeepSeek R1 Distill Llama 8B to the next level, delivering 10–15% higher throughput, improved time-to-first-token (TTFT), and more stable performance at lower batch sizes, an area that previously needed a boost. In short, Paiton further cements its ability to exploit AMD hardware’s raw power, bridging ...","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",[803,777,91,819,1194,1195,1018,1019,1084,91,783],"Deepseek","H100",{"path":1197,"title":1198,"description":1199,"date":1200,"slug":1201,"image":1202,"originalUrl":1203,"categories":1204},"\u002Fblog\u002Fa-first-look-at-paiton-in-action-deepseek-r1-distill-llama-3-1-8b","A First Look at Paiton in Action: Deepseek R1 Distill Llama 3.1 8B","Outperforming Stock Models on the AMD MI300X 1. Introduction We couldn’t wait to show what Paiton can really do. After detailing our AMD-centric approach and architecture-level optimizations in our previous blog post, we decided to test-drive Paiton on a hype-worthy model: Deepseek R1 Distill Llama 3.1 8B. By compiling the model into efficient libraries and fusing ...","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",[803,777,91,819,1194,1195,1018,1019,1084,91,783],{"path":1206,"title":1207,"description":1208,"date":1209,"slug":1210,"image":1211,"originalUrl":1212,"categories":1213},"\u002Fblog\u002Fai-model-optimization-with-paiton","AI Model Optimization with Paiton","In the fast-paced world of artificial intelligence, model efficiency and performance are paramount. At ElioVP, we’re redefining what’s possible by delivering unparalleled optimization solutions for AI models with Paiton. By compiling the model’s architecture and leveraging our custom-written kernels, Paiton enables faster inference and reduced resource consumption on AMD GPUs. Why Model Optimization is More ...","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",[803,777,91,819,1195,1018,1019,1084,91,783],1788941674398]