[{"data":1,"prerenderedAt":1199},["ShallowReactive",2],{"blog-post-en-\u002Fblog\u002Fpaiton-qwen-image-21-radeon-ai-pro-r9700":3,"blog-posts-sidebar-en":710},{"id":4,"title":5,"body":6,"categories":690,"date":695,"description":696,"extension":697,"heading":698,"image":699,"meta":700,"navigation":701,"originalUrl":702,"path":703,"seo":704,"slug":705,"socialImage":706,"stem":707,"updated":708,"__hash__":709},"blog\u002Fblog\u002Fpaiton-qwen-image-21-radeon-ai-pro-r9700.md","Qwen-Image 2.1 on 1 × Radeon AI PRO R9700: 2048×2048 images in 103 seconds",{"type":7,"value":8,"toc":683},"minimark",[9,22,33,70,75,82,98,185,199,216,220,229,252,303,310,356,359,365,384,413,416,420,441,472,496,500,520,538,556,679],[10,11,12,13,17,18,21],"p",{},"When an image is generated on your own workstation, you can revise the prompt and try again without sending the request to a cloud image service. The released ",[14,15,16],"strong",{},"Qwen-Image 2.1 v1.0.2"," package makes 2048 × 2048 image generation practical with ",[14,19,20],{},"1 × Radeon AI PRO R9700 (32 GB)",".",[10,23,24,25,28,29,32],{},"In three fresh containers, the default profile's median warm request took ",[14,26,27],{},"103.29 seconds",", from submitting the image request through receiving the PNG. That is about ",[14,30,31],{},"37% less time"," than the separate, historical v1.0.1 container repeat at 165.14 seconds. The first request took 113.95 seconds. These are measured release results for this one GPU and workload, not a promise for every prompt or another card.",[10,34,35,36,39,40,44,45,49,50,62],{},"The checkpoint, resolution and 40 denoising steps stay the same. The text encoder, image model and VAE remain on the GPU: ",[14,37,38],{},"no CPU offload or VAE tiling",". The default profile gains speed by deliberately using lower precision ",[41,42,43],"em",{},"after"," the first seven denoising steps. Choose the ",[46,47,48],"code",{},"exact"," profile when more precision margin matters. Both options are available in the public container.",[51,52,53],"sup",{},[54,55,61],"a",{"href":56,"ariaDescribedBy":57,"dataFootnoteRef":59,"id":60},"#user-content-fn-guide",[58],"footnote-label","","user-content-fnref-guide","1",[51,63,64],{},[54,65,69],{"href":66,"ariaDescribedBy":67,"dataFootnoteRef":59,"id":68},"#user-content-fn-bench",[58],"user-content-fnref-bench","2",[71,72,74],"h2",{"id":73},"a-complete-image-request-measured","A complete image request, measured",[10,76,77],{},[78,79],"img",{"alt":80,"src":81},"Three separate Qwen-Image 2.1 container validation results on one R9700: v1.0.1 historical repeat 165.14 seconds warm, v1.0.2 exact 133.74 seconds, and v1.0.2 default 103.29 seconds. Lower is better.","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-qwen-image-21\u002Fwarm-request.webp",[10,83,84],{},[41,85,86,87,93,94,21],{},"2048 × 2048 pixels, 40 steps, guidance 1.0, batch one, unchanged balanced MXFP4 checkpoint, one 32 GB R9700. Bars show warm complete HTTP request time through PNG delivery. These are separate release repeats, not one matched experiment. ",[54,88,92],{"href":89,"rel":90},"https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin\u002Fblob\u002F6586aa618610aed596d2720fbb5768c181bf1011\u002Fmodels\u002FQwen-Image-2.1\u002FBENCHMARKS.md#container-validation",[91],"nofollow","Published measurements"," · ",[54,95,97],{"href":96},"\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-qwen-image-21\u002Fresults.json","chart data",[99,100,101,124],"table",{},[102,103,104],"thead",{},[105,106,107,111,115,118,121],"tr",{},[108,109,110],"th",{},"Published container runtime",[108,112,114],{"align":113},"right","Warm request",[108,116,117],{"align":113},"First request",[108,119,120],{"align":113},"Peak VRAM",[108,122,123],{},"Samples",[125,126,127,145,164],"tbody",{},[105,128,129,133,136,139,142],{},[130,131,132],"td",{},"v1.0.1 historical repeat",[130,134,135],{"align":113},"165.14 s",[130,137,138],{"align":113},"179.32 s",[130,140,141],{"align":113},"28.34 GiB",[130,143,144],{},"3 processes",[105,146,147,152,155,158,161],{},[130,148,149,150],{},"v1.0.2 ",[46,151,48],{},[130,153,154],{"align":113},"133.74 s",[130,156,157],{"align":113},"147.83 s",[130,159,160],{"align":113},"25.33 GiB",[130,162,163],{},"1 container",[105,165,166,169,174,179,182],{},[130,167,168],{},"v1.0.2 default",[130,170,171],{"align":113},[14,172,173],{},"103.29 s",[130,175,176],{"align":113},[14,177,178],{},"113.95 s",[130,180,181],{"align":113},"25.55 GiB",[130,183,184],{},"3 containers",[10,186,187,188,191,192,194,195,198],{},"The three-process timings are ",[14,189,190],{},"medians","; ",[46,193,48],{}," is one validation run. VRAM is the ",[14,196,197],{},"maximum sampled whole-device usage"," across each row's requests, sampled at nominal 5 ms intervals. The clock starts when the request is submitted and stops after the complete PNG response is received. It includes text encoding, image generation, VAE decoding and PNG delivery. It excludes model download and server startup.",[10,200,201,202,205,206,209,210],{},"The 37% figure compares separate published-container release repeats. The source also reports controlled, matched checks within v1.0.2, but those have a different sample design and values. We do not combine the two into a single matched benchmark. The earlier ",[14,203,204],{},"103.64-second"," qualification result is distinct from the ",[14,207,208],{},"103.29-second"," published-container result shown here.",[51,211,212],{},[54,213,69],{"href":66,"ariaDescribedBy":214,"dataFootnoteRef":59,"id":215},[58],"user-content-fnref-bench-2",[71,217,219],{"id":218},"start-creating-on-your-r9700","Start creating on your R9700",[10,221,222,223,228],{},"Prefer a visual workspace? ",[54,224,227],{"href":225,"rel":226},"https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-studio",[91],"Paiton Studio"," brings supported local image, video and writing tools together in your browser. Check its current tool list for model availability. The commands below run the specific Qwen-Image 2.1 container measured in this article.",[10,230,231,232,235,236,239,240,245,246],{},"You need Linux, Docker, a working AMD GPU driver and at least ",[14,233,234],{},"30 GiB of free device memory before loading",". Run one image worker at a time. The first launch downloads and verifies about ",[14,237,238],{},"9.33 GB"," of checkpoint files; later starts reuse the persistent cache. From a checkout of the ",[54,241,244],{"href":242,"rel":243},"https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin\u002Ftree\u002Fmain\u002Fmodels\u002FQwen-Image-2.1",[91],"public model guide",":",[51,247,248],{},[54,249,61],{"href":56,"ariaDescribedBy":250,"dataFootnoteRef":59,"id":251},[58],"user-content-fnref-guide-2",[253,254,258],"pre",{"className":255,"code":256,"language":257,"meta":59,"style":59},"language-bash shiki shiki-themes github-light github-dark","git clone https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin.git\ncd paiton-vllm-plugin\ngit checkout 6586aa618610aed596d2720fbb5768c181bf1011\n.\u002Fmodels\u002FQwen-Image-2.1\u002Fserve-docker.sh\n","bash",[46,259,260,276,286,297],{"__ignoreMap":59},[261,262,265,269,273],"span",{"class":263,"line":264},"line",1,[261,266,268],{"class":267},"sScJk","git",[261,270,272],{"class":271},"sZZnC"," clone",[261,274,275],{"class":271}," https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin.git\n",[261,277,279,283],{"class":263,"line":278},2,[261,280,282],{"class":281},"sj4cs","cd",[261,284,285],{"class":271}," paiton-vllm-plugin\n",[261,287,289,291,294],{"class":263,"line":288},3,[261,290,268],{"class":267},[261,292,293],{"class":271}," checkout",[261,295,296],{"class":271}," 6586aa618610aed596d2720fbb5768c181bf1011\n",[261,298,300],{"class":263,"line":299},4,[261,301,302],{"class":267},".\u002Fmodels\u002FQwen-Image-2.1\u002Fserve-docker.sh\n",[10,304,305,306,309],{},"Wait for the ",[46,307,308],{},"READY"," message. In another terminal, submit a prompt and save the PNG:",[253,311,313],{"className":255,"code":312,"language":257,"meta":59,"style":59},"python3 models\u002FQwen-Image-2.1\u002Frequest.py \\\n  --prompt 'A neon shop sign that reads \"QWEN IMAGE 2.1\", rainy night, reflections on wet pavement' \\\n  --size 2048 --seed 42 --output outputs\u002Fneon.png\n",[46,314,315,326,336],{"__ignoreMap":59},[261,316,317,320,323],{"class":263,"line":264},[261,318,319],{"class":267},"python3",[261,321,322],{"class":271}," models\u002FQwen-Image-2.1\u002Frequest.py",[261,324,325],{"class":281}," \\\n",[261,327,328,331,334],{"class":263,"line":278},[261,329,330],{"class":281},"  --prompt",[261,332,333],{"class":271}," 'A neon shop sign that reads \"QWEN IMAGE 2.1\", rainy night, reflections on wet pavement'",[261,335,325],{"class":281},[261,337,338,341,344,347,350,353],{"class":263,"line":288},[261,339,340],{"class":281},"  --size",[261,342,343],{"class":281}," 2048",[261,345,346],{"class":281}," --seed",[261,348,349],{"class":281}," 42",[261,351,352],{"class":281}," --output",[261,354,355],{"class":271}," outputs\u002Fneon.png\n",[10,357,358],{},"Run the client from the same repository directory. Give each new request a new output filename; the client does not overwrite an existing image.",[10,360,361,362,364],{},"For more precision margin, stop the default worker and start the same released image with the ",[46,363,48],{}," profile:",[253,366,368],{"className":255,"code":367,"language":257,"meta":59,"style":59},".\u002Fmodels\u002FQwen-Image-2.1\u002Fserve-docker.sh serve --precision-profile exact\n",[46,369,370],{"__ignoreMap":59},[261,371,372,375,378,381],{"class":263,"line":264},[261,373,374],{"class":267},".\u002Fmodels\u002FQwen-Image-2.1\u002Fserve-docker.sh",[261,376,377],{"class":271}," serve",[261,379,380],{"class":281}," --precision-profile",[261,382,383],{"class":271}," exact\n",[10,385,386,387,390,391,394,395,398,399,406,407],{},"It measured ",[14,388,389],{},"133.74 seconds warm"," in its one published-container validation run. The image API also accepts JSON at ",[46,392,393],{},"POST http:\u002F\u002F127.0.0.1:8191\u002Fv1\u002Fimages\u002Fgenerations",". The included ",[46,396,397],{},"request.py"," client handles the base64 response and writes the PNG. This ",[14,400,401,402,405],{},"image API is separate from ",[46,403,404],{},"paiton serve"," language-model presets","; use these model-specific launchers and request commands.",[51,408,409],{},[54,410,61],{"href":56,"ariaDescribedBy":411,"dataFootnoteRef":59,"id":412},[58],"user-content-fnref-guide-3",[10,414,415],{},"The qualified package also supports transparent PNG generation at 1024 or 2048 square and editing one input image to a 1024-square output. Those modes have their own quality and performance boundaries; the 103.29-second figure is for the text-to-image workload above.",[71,417,419],{"id":418},"why-the-default-is-faster-and-when-to-choose-exact","Why the default is faster, and when to choose exact",[10,421,422,423,425,426,428,429,435],{},"The model weights do not change between these runtime profiles. In the default, the cached text pass and first seven denoising steps use the exact path. Later steps use lower-precision calculations. This reduces the time spent generating while keeping the early composition steps at the ",[46,424,48],{}," profile's precision for the same quantized checkpoint. ",[46,427,48],{}," keeps that arithmetic through all 40 steps.",[51,430,431],{},[54,432,61],{"href":56,"ariaDescribedBy":433,"dataFootnoteRef":59,"id":434},[58],"user-content-fnref-guide-4",[51,436,437],{},[54,438,69],{"href":66,"ariaDescribedBy":439,"dataFootnoteRef":59,"id":440},[58],"user-content-fnref-bench-3",[10,442,443,444,450,451,454,455,457,458,461,462,465,466],{},"The default passed a ",[14,445,446,447,449],{},"bounded quality screen against images from its own ",[46,448,48],{}," profile",". It was not a test against the original unquantized BF16 weights, nor a guarantee of identical pixels or quality on arbitrary prompts. The 2048-square transparent RGBA case passed only narrowly: ",[14,452,453],{},"35.02 dB PSNR against a 35 dB threshold",". For transparent artwork or other work needing more margin, use ",[46,456,48],{}," at about ",[14,459,460],{},"134 seconds warm"," in the container check. The guide also documents an intermediate ",[46,463,464],{},"schedule-int8-11"," option.",[51,467,468],{},[54,469,69],{"href":66,"ariaDescribedBy":470,"dataFootnoteRef":59,"id":471},[58],"user-content-fnref-bench-4",[10,473,474,475,481,482,488],{},"Both profiles keep all model components GPU-resident. The reported 25.55 GiB default peak and 25.33 GiB exact peak are whole-device samples, not model download sizes or a promise that a smaller card will work. The released launcher qualifies the ",[14,476,477,478],{},"R9700 with RDNA4 ",[46,479,480],{},"gfx1201",", not every Radeon or every 32 GB GPU.",[51,483,484],{},[54,485,61],{"href":56,"ariaDescribedBy":486,"dataFootnoteRef":59,"id":487},[58],"user-content-fnref-guide-5",[51,489,490],{},[54,491,495],{"href":492,"ariaDescribedBy":493,"dataFootnoteRef":59,"id":494},"#user-content-fn-model",[58],"user-content-fnref-model","3",[71,497,499],{"id":498},"inspect-the-release-and-its-terms","Inspect the release and its terms",[10,501,502,503,507,508,513,514,519],{},"The ",[54,504,506],{"href":242,"rel":505},[91],"setup guide"," contains the current container, direct-generation, editing and offline instructions. The ",[54,509,512],{"href":510,"rel":511},"https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin\u002Fblob\u002Fmain\u002Fmodels\u002FQwen-Image-2.1\u002FBENCHMARKS.md",[91],"benchmark report"," gives per-run timing, memory, quality checks and limits. The ",[54,515,518],{"href":516,"rel":517},"https:\u002F\u002Fhuggingface.co\u002FEliovpAI\u002FQwen_Image-2.1-MXFP4-Paiton-RDNA4",[91],"Hugging Face companion"," provides the checkpoint and model card; the v1.0.2 runtime comes from the public container, not the card's original packaged loader.",[10,521,502,522,525,526,532],{},[14,523,524],{},"Qwen Research License"," on the model weights permits noncommercial research and evaluation; commercial use requires a separate upstream license. The Paiton runtime and adapter have their own Apache-2.0 terms. Review both before deployment. The model is downloaded separately when the container starts.",[51,527,528],{},[54,529,61],{"href":56,"ariaDescribedBy":530,"dataFootnoteRef":59,"id":531},[58],"user-content-fnref-guide-6",[51,533,534],{},[54,535,495],{"href":492,"ariaDescribedBy":536,"dataFootnoteRef":59,"id":537},[58],"user-content-fnref-model-2",[10,539,540,541,545,546,550,551,555],{},"This release is another option for local image work alongside our ",[54,542,544],{"href":543},"\u002Fblog\u002Fpaiton-flux2-klein-radeon-ai-pro-r9700","FLUX.2 klein profile",". Its timing boundary, model, settings and licensing are different, so the headline numbers should not be compared as if they were the same workload. Explore ",[54,547,549],{"href":548},"\u002Fproducts\u002Fpaiton","Paiton"," or ",[54,552,554],{"href":553},"\u002Fcontact","contact us"," to discuss an AMD image workload.",[557,558,561,566],"section",{"className":559,"dataFootnotes":59},[560],"footnotes",[71,562,565],{"className":563,"id":58},[564],"sr-only","Footnotes",[567,568,569,625,659],"ol",{},[570,571,573,578,579,586,587,586,594,586,601,586,609,586,617],"li",{"id":572},"user-content-fn-guide",[54,574,577],{"href":575,"rel":576},"https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin\u002Fblob\u002F6586aa618610aed596d2720fbb5768c181bf1011\u002Fmodels\u002FQwen-Image-2.1\u002FREADME.md",[91],"Qwen-Image 2.1 public setup and release guide",". ",[54,580,585],{"href":581,"ariaLabel":582,"className":583,"dataFootnoteBackref":59},"#user-content-fnref-guide","Back to reference 1",[584],"data-footnote-backref","↩"," ",[54,588,585,592],{"href":589,"ariaLabel":590,"className":591,"dataFootnoteBackref":59},"#user-content-fnref-guide-2","Back to reference 1-2",[584],[51,593,69],{},[54,595,585,599],{"href":596,"ariaLabel":597,"className":598,"dataFootnoteBackref":59},"#user-content-fnref-guide-3","Back to reference 1-3",[584],[51,600,495],{},[54,602,585,606],{"href":603,"ariaLabel":604,"className":605,"dataFootnoteBackref":59},"#user-content-fnref-guide-4","Back to reference 1-4",[584],[51,607,608],{},"4",[54,610,585,614],{"href":611,"ariaLabel":612,"className":613,"dataFootnoteBackref":59},"#user-content-fnref-guide-5","Back to reference 1-5",[584],[51,615,616],{},"5",[54,618,585,622],{"href":619,"ariaLabel":620,"className":621,"dataFootnoteBackref":59},"#user-content-fnref-guide-6","Back to reference 1-6",[584],[51,623,624],{},"6",[570,626,628,578,633,586,638,586,645,586,652],{"id":627},"user-content-fn-bench",[54,629,632],{"href":630,"rel":631},"https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin\u002Fblob\u002F6586aa618610aed596d2720fbb5768c181bf1011\u002Fmodels\u002FQwen-Image-2.1\u002FBENCHMARKS.md",[91],"Qwen-Image 2.1 v1.0.2 benchmark report",[54,634,585],{"href":635,"ariaLabel":636,"className":637,"dataFootnoteBackref":59},"#user-content-fnref-bench","Back to reference 2",[584],[54,639,585,643],{"href":640,"ariaLabel":641,"className":642,"dataFootnoteBackref":59},"#user-content-fnref-bench-2","Back to reference 2-2",[584],[51,644,69],{},[54,646,585,650],{"href":647,"ariaLabel":648,"className":649,"dataFootnoteBackref":59},"#user-content-fnref-bench-3","Back to reference 2-3",[584],[51,651,495],{},[54,653,585,657],{"href":654,"ariaLabel":655,"className":656,"dataFootnoteBackref":59},"#user-content-fnref-bench-4","Back to reference 2-4",[584],[51,658,608],{},[570,660,662,578,667,586,672],{"id":661},"user-content-fn-model",[54,663,666],{"href":664,"rel":665},"https:\u002F\u002Fhuggingface.co\u002FEliovpAI\u002FQwen_Image-2.1-MXFP4-Paiton-RDNA4\u002Fblob\u002Fb4f6bfc00ca13391eb782cb08994a6c7f8bc28bc\u002FREADME.md",[91],"Published Hugging Face model card, reviewed revision",[54,668,585],{"href":669,"ariaLabel":670,"className":671,"dataFootnoteBackref":59},"#user-content-fnref-model","Back to reference 3",[584],[54,673,585,677],{"href":674,"ariaLabel":675,"className":676,"dataFootnoteBackref":59},"#user-content-fnref-model-2","Back to reference 3-2",[584],[51,678,69],{},[680,681,682],"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":59,"searchDepth":278,"depth":278,"links":684},[685,686,687,688,689],{"id":73,"depth":278,"text":74},{"id":218,"depth":278,"text":219},{"id":418,"depth":278,"text":419},{"id":498,"depth":278,"text":499},{"id":58,"depth":278,"text":565},[549,691,692,693,694],"AMD Radeon","Local AI","Image Generation","Qwen","2026-09-23T09:00:00Z","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.","md","2048 × 2048 images in 103 seconds. 1 × Radeon AI PRO R9700.","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-qwen-image-21\u002Fhero.webp",{},true,"https:\u002F\u002Feliovp.com\u002Fblog\u002Fpaiton-qwen-image-21-radeon-ai-pro-r9700","\u002Fblog\u002Fpaiton-qwen-image-21-radeon-ai-pro-r9700",{"title":5,"description":696},"paiton-qwen-image-21-radeon-ai-pro-r9700","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-qwen-image-21\u002Fsocial.webp","blog\u002Fpaiton-qwen-image-21-radeon-ai-pro-r9700",null,"6nnF2MNMOmIIqefM9sfw-y1_ohmTBuAYA71t8rxQlJc",[711,713,726,736,748,756,771,780,794,826,838,860,878,897,915,933,950,962,978,993,1005,1014,1022,1037,1049,1060,1071,1081,1094,1104,1117,1128,1138,1149,1158,1170,1181,1190],{"path":703,"title":5,"description":696,"date":695,"slug":705,"image":699,"originalUrl":702,"categories":712},[549,691,692,693,694],{"path":714,"title":715,"description":716,"date":717,"slug":718,"image":719,"originalUrl":720,"categories":721},"\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",[549,691,722,723,724,725],"vLLM","Qwen3.8","Inference Optimization","DFlash2",{"path":727,"title":728,"description":729,"date":730,"slug":731,"image":732,"originalUrl":733,"categories":734},"\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",[549,691,692,735,722],"GGUF",{"path":737,"title":738,"description":739,"date":740,"slug":741,"image":742,"originalUrl":743,"categories":744},"\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",[549,691,692,745,746,747],"Video Generation","MiniMax H3","ComfyUI",{"path":543,"title":749,"description":750,"date":751,"slug":752,"image":753,"originalUrl":543,"categories":754},"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",[549,691,692,693,755,747],"FLUX",{"path":757,"title":758,"description":759,"date":760,"slug":761,"image":762,"originalUrl":763,"categories":764},"\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",[549,765,691,766,767,768,724,769,722,770],"Artificial Intelligence","AI Inference","GPU Performance","Inference Latency","Large Language Models","Cost Efficiency",{"path":772,"title":773,"description":774,"date":775,"slug":776,"image":777,"originalUrl":778,"categories":779},"\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",[549,765,691,766,767,768,724,769,722,770],{"path":781,"title":782,"description":783,"date":784,"slug":785,"image":786,"originalUrl":708,"categories":787},"\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",[788,789,790,791,792,793],"All","AI Infrastructure","Data Centers","ModFlex","HPC","AMD Helios",{"path":795,"title":796,"description":797,"date":798,"slug":799,"image":800,"originalUrl":801,"categories":802},"\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",[788,765,549,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,549,820,821,822,823,824,825],"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":827,"title":828,"description":829,"date":830,"slug":831,"image":832,"originalUrl":833,"categories":834},"\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",[788,765,835,836,804,837,835,817],"Modular DC","Uncategorized","De Tijd",{"path":839,"title":840,"description":841,"date":842,"slug":843,"image":844,"originalUrl":845,"categories":846},"\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",[788,765,847,836,848,849,850,851,852,853,854,855,810,856,811,857,858,859],"Trending","AI Act","Anthropomorphism","AVG","Benelux","ChatGPT","Cybersecurity","Data Governance","Data Security","GDPR","Microsoft Copilot","Privacy","Shadow AI",{"path":861,"title":862,"description":863,"date":864,"slug":865,"image":866,"originalUrl":867,"categories":868},"\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",[788,869,870,871,853,872,873,874,856,875,876,877,858],"AWS","Belgian Mobile ID","Cloud Act","Data Sovereignty","Digital Identity","eIDAS","itsme","Liberty Global","MyGov.be",{"path":879,"title":880,"description":881,"date":882,"slug":883,"image":884,"originalUrl":885,"categories":886},"\u002Fblog\u002Ffield-report-the-reality-of-building-agentic-ai-in-2025","Field Report. 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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",[788,765,847,789,906,907,908,909,910,911,912,913,914],"AI Neocloud","Circular Financing","GPU Cloud","Investment Risks","Startup Valuation","Synthetic Bubble","Tech Analysis","Vaporware","Venture Capital",{"path":916,"title":917,"description":918,"date":919,"slug":920,"image":921,"originalUrl":922,"categories":923},"\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",[788,835,836,924,789,925,926,927,928,929,930,931,932],"150kW Rack","DLC","High Density","Liquid Cooling","Modular Data Center","NVIDIA Blackwell Ultra","NVIDIA GB300","NVL72","Rapid Deployment",{"path":934,"title":935,"description":936,"date":937,"slug":938,"image":939,"originalUrl":940,"categories":941},"\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",[788,765,549,836,942,765,943,944,945,946,947,948,549,949],"AMD MI300X","CUDA Translation","FP8","GPU Optimization","High Performance Computing","HIP","Kernel Tuning","ROCm",{"path":951,"title":952,"description":953,"date":954,"slug":955,"image":956,"originalUrl":957,"categories":958},"\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",[788,765,549,766,959,942,770,960,724,948,549,961,722],"AMD Instinct","High Throughput","SGLang",{"path":963,"title":964,"description":965,"date":966,"slug":967,"image":968,"originalUrl":969,"categories":970},"\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",[788,765,549,971,942,972,724,973,974,975,976,549,977],"AI Benchmarks","Cost per Token","Mixture of Experts","MoE","NVIDIA B200","NVIDIA H200","Qwen3",{"path":979,"title":980,"description":981,"date":982,"slug":983,"image":984,"originalUrl":985,"categories":986},"\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",[788,765,887,836,888,987,988,989,990,893,895,820,991,992],"Damage Detection","Document Processing","Email Automation","Invoice Extraction","Ticket Automation","Workflow Automation",{"path":994,"title":995,"description":996,"date":997,"slug":998,"image":999,"originalUrl":1000,"categories":1001},"\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",[788,765,549,836,1002,804,805,1003,1004,816,817,549,722],"AI","H200","MI300X",{"path":1006,"title":1007,"description":1008,"date":1009,"slug":1010,"image":1011,"originalUrl":1012,"categories":1013},"\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",[788,765,887,888,987,988,989,990,893,895,820,991,992],{"path":1015,"title":1016,"description":1017,"date":1018,"slug":1019,"image":59,"originalUrl":1020,"categories":1021},"\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",[788,765,549],{"path":1023,"title":1024,"description":1025,"date":1026,"slug":1027,"image":1028,"originalUrl":1029,"categories":1030},"\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",[788,765,549,836,766,942,1031,944,1032,1033,1034,549,1035,1036],"Cold Start","Graph Compilation","Llama 3.1 405B","LLM Optimization","Startup Latency","Tensor Parallelism",{"path":1038,"title":1039,"description":1040,"date":1041,"slug":1042,"image":1043,"originalUrl":1044,"categories":1045},"\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",[788,765,549,836,942,1046,892,811,767,768,769,1033,1047,1048],"Cold Start Optimization","Model Serving","vLLM Optimization",{"path":1050,"title":1051,"description":1052,"date":1053,"slug":1054,"image":1055,"originalUrl":1056,"categories":1057},"\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",[788,765,549,887,836,804,1004,817,1058,1059],"RX7900XTX","tenstorrent",{"path":1061,"title":1062,"description":1063,"date":1064,"slug":1065,"image":1066,"originalUrl":1067,"categories":1068},"\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",[788,765,835,887,805,1003,1069,817,1070],"MI325x","pnl calculator",{"path":1072,"title":1073,"description":1074,"date":1075,"slug":1076,"image":1077,"originalUrl":1078,"categories":1079},"\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",[788,765,549,1002,804,1003,1080,817,549,722],"MI300",{"path":1082,"title":1083,"description":1084,"date":1085,"slug":1086,"image":1087,"originalUrl":1088,"categories":1089},"\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 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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",[788,847,1002,804,1113,1114,1115,1116],"import","Taiwan","Tariffs","Trump",{"path":1118,"title":1119,"description":1120,"date":1121,"slug":1122,"image":1123,"originalUrl":1124,"categories":1125},"\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",[788,765,887,1002,1126,1127],"AI Agents","ERP",{"path":1129,"title":1130,"description":1131,"date":1132,"slug":1133,"image":1134,"originalUrl":1135,"categories":1136},"\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",[788,765,847,1137,804,812,817],"AI news",{"path":1139,"title":1140,"description":1141,"date":1142,"slug":1143,"image":1144,"originalUrl":1145,"categories":1146},"\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",[788,765,549,1002,804,1147,1148,1004,549],"benchmark","LLM",{"path":1150,"title":1151,"description":1152,"date":1153,"slug":1154,"image":1155,"originalUrl":1156,"categories":1157},"\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",[788,765,549],{"path":1159,"title":1160,"description":1161,"date":1162,"slug":1163,"image":1164,"originalUrl":1165,"categories":1166},"\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",[788,804,1167,1168,1169],"Jim Greene","Podcast","Tech 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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",[788,765,549,804,1179,1180,1003,1004,1069,549,722],{"path":1191,"title":1192,"description":1193,"date":1194,"slug":1195,"image":1196,"originalUrl":1197,"categories":1198},"\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",[788,765,549,804,1180,1003,1004,1069,549,722],1790198252670]