[{"data":1,"prerenderedAt":1742},["ShallowReactive",2],{"blog-post-en-\u002Fblog\u002Fpaiton-qwen38-neo-gguf-vllm-r9700":3,"blog-posts-sidebar-en":1263},{"id":4,"title":5,"body":6,"categories":1246,"date":1251,"description":1252,"extension":1253,"heading":1254,"image":1255,"meta":1256,"navigation":695,"originalUrl":1257,"path":1258,"seo":1259,"slug":1260,"stem":1261,"updated":1254,"__hash__":1262},"blog\u002Fblog\u002Fpaiton-qwen38-neo-gguf-vllm-r9700.md","Qwen3.8 GGUF in vLLM: Faster Responses on One Radeon",{"type":7,"value":8,"toc":1233},"minimark",[9,28,42,61,64,69,78,81,86,102,113,116,137,142,146,157,166,169,176,187,191,206,212,223,304,313,316,325,328,333,342,351,355,366,387,393,404,457,466,470,481,506,509,513,528,598,605,614,618,624,635,650,720,733,736,839,848,852,855,864,869,882,885,1229],[10,11,12,16],"p",{},[13,14,15],"strong",{},"Original NEO CODER MAX weights. Paiton-compiled execution. Up to 6.4% lower warm request latency than our matched llama.cpp baseline on a Radeon AI PRO R9700.",[17,18,19],"sup",{},[20,21,27],"a",{"href":22,"ariaDescribedBy":23,"dataFootnoteRef":25,"id":26},"#user-content-fn-readme",[24],"footnote-label","","user-content-fnref-readme","1",[10,29,30,34],{},[31,32,33],"em",{},"Original GGUF weights. Actual vLLM serving. Paiton-compiled execution on one Radeon AI PRO R9700.",[17,35,36],{},[20,37,41],{"href":38,"ariaDescribedBy":39,"dataFootnoteRef":25,"id":40},"#user-content-fn-native",[24],"user-content-fnref-native","2",[10,43,44,45,53],{},"DavidAU’s Qwen3.8 NEO CODER MAX has attracted plenty of attention. On September 14, 2026, its Hugging Face repository showed 875,703 downloads in the preceding month. That is a repository download count, not a count of unique users, but it is a useful indication of the interest around this fine-tune.",[17,46,47],{},[20,48,52],{"href":49,"ariaDescribedBy":50,"dataFootnoteRef":25,"id":51},"#user-content-fn-model",[24],"user-content-fnref-model","3",[17,54,55],{},[20,56,60],{"href":57,"ariaDescribedBy":58,"dataFootnoteRef":25,"id":59},"#user-content-fn-downloads",[24],"user-content-fnref-downloads","4",[10,62,63],{},"We wanted to answer a different question from another model review:",[10,65,66],{},[13,67,68],{},"Can you keep the original GGUF fine-tune, serve it through vLLM, and still compete with llama.cpp on a single Radeon?",[10,70,71,72],{},"For this tested model and configuration, yes. Paiton runs the selected Q4_K_M checkpoint through native AMD execution inside vLLM, rather than routing requests to a separate llama.cpp server.",[17,73,74],{},[20,75,41],{"href":38,"ariaDescribedBy":76,"dataFootnoteRef":25,"id":77},[24],"user-content-fnref-native-2",[10,79,80],{},"The fine-tune is the author’s. The execution path is ours.",[82,83,85],"h2",{"id":84},"gguf-support-is-not-the-same-as-optimized-gguf-execution","GGUF support is not the same as optimized GGUF execution",[10,87,88,89,93,94],{},"vLLM already supports GGUF. Its current documentation nevertheless describes that support as “highly experimental and under-optimized,” with potential incompatibilities with other features. Support now lives in the upstream ",[90,91,92],"code",{},"vllm-gguf-plugin",".",[17,95,96],{},[20,97,101],{"href":98,"ariaDescribedBy":99,"dataFootnoteRef":25,"id":100},"#user-content-fn-vllm",[24],"user-content-fnref-vllm","5",[10,103,104,105],{},"That plugin already documents several model families, including related Qwen vision-language models. This is not an announcement that GGUF has become possible in vLLM for the first time.",[17,106,107],{},[20,108,112],{"href":109,"ariaDescribedBy":110,"dataFootnoteRef":25,"id":111},"#user-content-fn-upstream",[24],"user-content-fnref-upstream","6",[10,114,115],{},"Our focus is narrower: make this particular GGUF fine-tune run efficiently on AMD hardware without making users switch to a different checkpoint or a different serving framework.",[10,117,118,119,127,128,136],{},"GGUF is a container for weights and metadata, not a requirement to use one inference engine.",[17,120,121],{},[20,122,126],{"href":123,"ariaDescribedBy":124,"dataFootnoteRef":25,"id":125},"#user-content-fn-gguf",[24],"user-content-fnref-gguf","7"," And llama.cpp already provides its own OpenAI-compatible server.",[17,129,130],{},[20,131,135],{"href":132,"ariaDescribedBy":133,"dataFootnoteRef":25,"id":134},"#user-content-fn-llama",[24],"user-content-fnref-llama","8"," An API wrapper alone would not be the achievement here.",[10,138,139],{},[13,140,141],{},"The distinction is actual vLLM serving, backed by Paiton’s compiled native execution.",[82,143,145],{"id":144},"keep-the-fine-tune-change-the-execution-path","Keep the fine-tune. Change the execution path.",[10,147,148,149],{},"The selected checkpoint is the author’s original mixed Q4_K_M GGUF, including its higher-precision tensors and BF16 output head. We have not replaced it with base Qwen or converted it into a new AWQ checkpoint.",[17,150,151],{},[20,152,156],{"href":153,"ariaDescribedBy":154,"dataFootnoteRef":25,"id":155},"#user-content-fn-lock",[24],"user-content-fnref-lock","9",[10,158,159,160],{},"Paiton provides the native language and vision execution. vLLM retains the model-loading integration, scheduling, sampling and streaming interface. The compiler remains proprietary; the public release contains the runtime artifacts needed to use it.",[17,161,162],{},[20,163,41],{"href":38,"ariaDescribedBy":164,"dataFootnoteRef":25,"id":165},[24],"user-content-fnref-native-3",[10,167,168],{},"This is an optimization inside an existing serving stack, not a requirement to adopt another inference server.",[10,170,171],{},[172,173],"img",{"alt":174,"src":175},"High-level integration: original pinned GGUF weights, actual vLLM scheduling, sampling and streaming, Paiton-compiled native execution, and one Radeon AI PRO R9700. The proprietary compiler is not distributed.","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-neo-gguf\u002F02-native-execution-neo-gguf.webp",[10,177,178,181],{},[31,179,180],{},"The public integration at a glance: keep the author’s GGUF, serve through vLLM, and execute with Paiton on the R9700. This is an integration overview, not a disclosure of compiler internals.",[17,182,183],{},[20,184,41],{"href":38,"ariaDescribedBy":185,"dataFootnoteRef":25,"id":186},[24],"user-content-fnref-native-4",[82,188,190],{"id":189},"faster-complete-responses-not-just-a-faster-kernel","Faster complete responses, not just a faster kernel",[10,192,193,194,197,198],{},"The comparison below measures ",[13,195,196],{},"complete streaming HTTP requests",", with exactly 128 generated tokens. Both engines ran serially on the same idle R9700. Each workload had one warmup and five measured requests.",[17,199,200],{},[20,201,205],{"href":202,"ariaDescribedBy":203,"dataFootnoteRef":25,"id":204},"#user-content-fn-bench",[24],"user-content-fnref-bench","10",[10,207,208],{},[172,209],{"alt":210,"src":211},"Median warm complete-request latency with 128 output tokens. At 128 input tokens, llama.cpp takes 5.264 seconds and Paiton plus vLLM 4.925 seconds, 6.4% lower. At 1,024 input, 5.933 versus 5.631 seconds, 5.1% lower. At 4,096 input, 9.099 versus 9.023 seconds, 0.8% lower, near-parity. One warmup and five measured requests per workload.","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-neo-gguf\u002F01-text-latency-neo-gguf.webp",[10,213,214,217],{},[31,215,216],{},"Complete streaming requests, not kernel-only timings. Both engines generate exactly 128 tokens. The 4,096-token result is near-parity; these five-request samples do not establish statistical significance.",[17,218,219],{},[20,220,205],{"href":202,"ariaDescribedBy":221,"dataFootnoteRef":25,"id":222},[24],"user-content-fnref-bench-2",[224,225,226,246],"table",{},[227,228,229],"thead",{},[230,231,232,236,240,243],"tr",{},[233,234,235],"th",{},"Input tokens",[233,237,239],{"align":238},"right","llama.cpp median",[233,241,242],{"align":238},"Paiton + vLLM median",[233,244,245],{"align":238},"Request latency reduction",[247,248,249,268,286],"tbody",{},[230,250,251,255,258,263],{},[252,253,254],"td",{},"128",[252,256,257],{"align":238},"5.264 s",[252,259,260],{"align":238},[13,261,262],{},"4.925 s",[252,264,265],{"align":238},[13,266,267],{},"6.4%",[230,269,270,273,276,281],{},[252,271,272],{},"1,024",[252,274,275],{"align":238},"5.933 s",[252,277,278],{"align":238},[13,279,280],{},"5.631 s",[252,282,283],{"align":238},[13,284,285],{},"5.1%",[230,287,288,291,294,299],{},[252,289,290],{},"4,096",[252,292,293],{"align":238},"9.099 s",[252,295,296],{"align":238},[13,297,298],{},"9.023 s",[252,300,301],{"align":238},[13,302,303],{},"0.8%",[10,305,306,307],{},"These are warm-request measurements, not first-install or cold-start timings.",[17,308,309],{},[20,310,205],{"href":202,"ariaDescribedBy":311,"dataFootnoteRef":25,"id":312},[24],"user-content-fnref-bench-3",[10,314,315],{},"The 4,096-token result is best described as near-parity. A 76-millisecond median difference in a small sample is not a basis for a sweeping performance claim.",[10,317,318,319],{},"Nor does lower complete-request latency mean every part of every request is faster: llama.cpp still wins some single-output, prefill-dominated tests.",[17,320,321],{},[20,322,41],{"href":38,"ariaDescribedBy":323,"dataFootnoteRef":25,"id":324},[24],"user-content-fnref-native-5",[10,326,327],{},"The useful result is that this GGUF can stay inside vLLM while delivering competitive latency. In these measured full-response cases, it was also lower.",[329,330,332],"h3",{"id":331},"what-was-held-constant","What was held constant?",[10,334,335,336],{},"The comparison used the same pinned GGUF, source tokenizer, 8,192-token context, 2,048-token prefill chunks and BF16 KV cache. Both engines had one active sequence, MTP and prefix caching disabled, and greedy sampling with fixed token counts. llama.cpp was an unmodified HIP build, not a CPU fallback.",[17,337,338],{},[20,339,205],{"href":202,"ariaDescribedBy":340,"dataFootnoteRef":25,"id":341},[24],"user-content-fnref-bench-4",[10,343,344,345],{},"The fixed-length prompts are synthetic timing workloads, not a coding-productivity benchmark. Shorter reasoning or earlier stopping is not being counted as faster execution.",[17,346,347],{},[20,348,205],{"href":202,"ariaDescribedBy":349,"dataFootnoteRef":25,"id":350},[24],"user-content-fnref-bench-5",[82,352,354],{"id":353},"image-input-works-too","Image input works too",[10,356,357,358],{},"This release also accepts one PNG or JPEG image through the chat-completions interface. Its image encoder runs through the native Paiton path; image embeddings and generated text share the same context budget.",[17,359,360],{},[20,361,365],{"href":362,"ariaDescribedBy":363,"dataFootnoteRef":25,"id":364},"#user-content-fn-image",[24],"user-content-fnref-image","11",[10,367,368,369,372,373,376,377,380,381],{},"For a ",[13,370,371],{},"1,024 × 1,024 image and 128 output tokens",", median complete-request time was ",[13,374,375],{},"6.279 seconds with Paiton versus 6.455 seconds with llama.cpp",", about ",[13,378,379],{},"2.7% lower",". That includes image processing, language prefill, generation and serving overhead.",[17,382,383],{},[20,384,41],{"href":38,"ariaDescribedBy":385,"dataFootnoteRef":25,"id":386},[24],"user-content-fnref-native-6",[10,388,389],{},[172,390],{"alt":391,"src":392},"Median complete-request latency for a single image and 128 output tokens. A 256 by 256 image takes 5.111 seconds in llama.cpp versus 4.909 seconds in Paiton plus vLLM, 4.0% lower. A 1,024 by 1,024 image takes 6.455 versus 6.279 seconds, 2.7% lower. Five measured requests per case.","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-neo-gguf\u002F03-image-latency-neo-gguf.webp",[10,394,395,398],{},[31,396,397],{},"Two qualified image sizes, each followed by 128 generated tokens. Timing includes image encoding, language prefill, generation and serving overhead. This measures image understanding, not image generation.",[17,399,400],{},[20,401,205],{"href":202,"ariaDescribedBy":402,"dataFootnoteRef":25,"id":403},[24],"user-content-fnref-bench-6",[224,405,406,419],{},[227,407,408],{},[230,409,410,413,415,417],{},[233,411,412],{},"Image size",[233,414,239],{"align":238},[233,416,242],{"align":238},[233,418,245],{"align":238},[247,420,421,439],{},[230,422,423,426,429,434],{},[252,424,425],{},"256 × 256",[252,427,428],{"align":238},"5.111 s",[252,430,431],{"align":238},[13,432,433],{},"4.909 s",[252,435,436],{"align":238},[13,437,438],{},"4.0%",[230,440,441,444,447,452],{},[252,442,443],{},"1,024 × 1,024",[252,445,446],{"align":238},"6.455 s",[252,448,449],{"align":238},[13,450,451],{},"6.279 s",[252,453,454],{"align":238},[13,455,456],{},"2.7%",[10,458,459,460],{},"This is single-image inference, not video or unrestricted multi-image support.",[17,461,462],{},[20,463,365],{"href":362,"ariaDescribedBy":464,"dataFootnoteRef":25,"id":465},[24],"user-content-fnref-image-2",[82,467,469],{"id":468},"same-weights-does-not-mean-identical-arithmetic","Same weights does not mean identical arithmetic",[10,471,472,473],{},"Keeping a GGUF checkpoint unchanged does not make two runtimes numerically identical. The qualified execution profile uses different activation arithmetic from the older FP32 reference path; the weight values remain unchanged.",[17,474,475],{},[20,476,480],{"href":477,"ariaDescribedBy":478,"dataFootnoteRef":25,"id":479},"#user-content-fn-release",[24],"user-content-fnref-release","12",[10,482,483,484,487,488,491,492,498],{},"The published checks include ",[13,485,486],{},"10 of 11 fixed text tasks passed",", with the same failed task as llama.cpp, and ",[13,489,490],{},"five image fixtures passed",". The final prefill optimization also matched 31,784,960 compared logits against the preceding qualified Paiton profile, not against every other engine.",[17,493,494],{},[20,495,480],{"href":477,"ariaDescribedBy":496,"dataFootnoteRef":25,"id":497},[24],"user-content-fnref-release-2",[17,499,500],{},[20,501,505],{"href":502,"ariaDescribedBy":503,"dataFootnoteRef":25,"id":504},"#user-content-fn-publication",[24],"user-content-fnref-publication","13",[10,507,508],{},"Those are useful release checks, not proof of unchanged capability on every coding task, conversation or image. We keep the numerical comparisons and their reference profiles explicit rather than describing the release as universally bit-identical.",[82,510,512],{"id":511},"the-deployment-profile","The deployment profile",[10,514,515,516,522],{},"The published v1.1.0 release is qualified for the following configuration:",[17,517,518],{},[20,519,480],{"href":477,"ariaDescribedBy":520,"dataFootnoteRef":25,"id":521},[24],"user-content-fnref-release-3",[17,523,524],{},[20,525,365],{"href":362,"ariaDescribedBy":526,"dataFootnoteRef":25,"id":527},[24],"user-content-fnref-image-3",[224,529,530,540],{},[227,531,532],{},[230,533,534,537],{},[233,535,536],{},"Setting",[233,538,539],{},"Supported profile",[247,541,542,550,558,566,574,582,590],{},[230,543,544,547],{},[252,545,546],{},"GPU",[252,548,549],{},"One Radeon AI PRO R9700, gfx1201",[230,551,552,555],{},[252,553,554],{},"Model",[252,556,557],{},"Pinned original NEO CODER MAX Q4_K_M GGUF",[230,559,560,563],{},[252,561,562],{},"Runtime",[252,564,565],{},"Paiton; pinned ROCm 7.14.60850",[230,567,568,571],{},[252,569,570],{},"Context",[252,572,573],{},"8,192 tokens total",[230,575,576,579],{},[252,577,578],{},"Active sequences",[252,580,581],{},"One; additional HTTP requests queue",[230,583,584,587],{},[252,585,586],{},"Input",[252,588,589],{},"Text, or text with one PNG\u002FJPEG image",[230,591,592,595],{},[252,593,594],{},"MTP, prefix caching, video",[252,596,597],{},"Disabled",[10,599,600,601,604],{},"The model’s name includes MTP, but these results do ",[13,602,603],{},"not"," use speculative MTP decoding. Likewise, queued clients should not be confused with qualified multi-sequence GPU batching.",[10,606,607,608],{},"This release targets the 32 GB R9700. It is not a promise of support for a smaller GPU, another GGUF quantization, longer contexts or high-concurrency serving.",[17,609,610],{},[20,611,27],{"href":22,"ariaDescribedBy":612,"dataFootnoteRef":25,"id":613},[24],"user-content-fnref-readme-2",[82,615,617],{"id":616},"run-the-published-release","Run the published release",[10,619,620],{},[172,621],{"alt":622,"src":623},"The qualified local profile: one Radeon AI PRO R9700, Linux and Docker, an 8,192-token context, one active sequence, and text or one PNG\u002FJPEG image. An illustrative API view shows the local chat-completions endpoint and qwen38-neo model name. Not an application screenshot.","\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-neo-gguf\u002F05-local-endpoint-neo-gguf.webp",[10,625,626,629],{},[31,627,628],{},"The qualified local serving profile. The API panel is illustrative, not an application screenshot or benchmark trace. Compiler access is not needed to use the published runtime.",[17,630,631],{},[20,632,480],{"href":477,"ariaDescribedBy":633,"dataFootnoteRef":25,"id":634},[24],"user-content-fnref-release-4",[10,636,637,638,644],{},"On a Linux system with an R9700, a working AMD driver and Docker GPU access, clone the public helper repository and launch the pinned v1.1.0 image:",[17,639,640],{},[20,641,27],{"href":22,"ariaDescribedBy":642,"dataFootnoteRef":25,"id":643},[24],"user-content-fnref-readme-3",[17,645,646],{},[20,647,480],{"href":477,"ariaDescribedBy":648,"dataFootnoteRef":25,"id":649},[24],"user-content-fnref-release-5",[651,652,656],"pre",{"className":653,"code":654,"language":655,"meta":25,"style":25},"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\nPAITON_NEO_IMAGE=ghcr.io\u002Feliovp\u002Fpaiton-vllm-plugin@sha256:534287969135f581744ae481b578599468b0bf7ac9a4051b0941500e4c18da4d \\\n  .\u002Fmodels\u002FQwen3.8-NEO-CODER-MAX\u002Fserve-docker.sh\n","bash",[90,657,658,681,690,697,714],{"__ignoreMap":25},[659,660,663,667,671,675,678],"span",{"class":661,"line":662},"line",1,[659,664,666],{"class":665},"sScJk","git",[659,668,670],{"class":669},"sZZnC"," clone",[659,672,674],{"class":673},"sj4cs"," --depth",[659,676,677],{"class":673}," 1",[659,679,680],{"class":669}," https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin.git\n",[659,682,684,687],{"class":661,"line":683},2,[659,685,686],{"class":673},"cd",[659,688,689],{"class":669}," paiton-vllm-plugin\n",[659,691,693],{"class":661,"line":692},3,[659,694,696],{"emptyLinePlaceholder":695},true,"\n",[659,698,700,704,708,711],{"class":661,"line":699},4,[659,701,703],{"class":702},"sVt8B","PAITON_NEO_IMAGE",[659,705,707],{"class":706},"szBVR","=",[659,709,710],{"class":669},"ghcr.io\u002Feliovp\u002Fpaiton-vllm-plugin@sha256:534287969135f581744ae481b578599468b0bf7ac9a4051b0941500e4c18da4d",[659,712,713],{"class":665}," \\\n",[659,715,717],{"class":661,"line":716},5,[659,718,719],{"class":669},"  .\u002Fmodels\u002FQwen3.8-NEO-CODER-MAX\u002Fserve-docker.sh\n",[10,721,722,723,726,727],{},"The first start downloads approximately ",[13,724,725],{},"19.43 GB"," of pinned language and projector files. Later starts reuse the cache and verify the hashes. No compiler checkout is required. An existing copy of the exact pinned GGUF can also be mounted and reused using the model guide’s instructions.",[17,728,729],{},[20,730,27],{"href":22,"ariaDescribedBy":731,"dataFootnoteRef":25,"id":732},[24],"user-content-fnref-readme-4",[10,734,735],{},"After the server is ready, send a request from another terminal:",[651,737,739],{"className":653,"code":738,"language":655,"meta":25,"style":25},"curl http:\u002F\u002F127.0.0.1:8000\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H 'Content-Type: application\u002Fjson' \\\n  -d '{\n    \"model\": \"qwen38-neo\",\n    \"messages\": [\n      {\n        \"role\": \"user\",\n        \"content\": \"Write a Python function that removes duplicate integers while preserving their original order.\"\n      }\n    ],\n    \"temperature\": 0,\n    \"max_tokens\": 256,\n    \"stream\": true,\n    \"chat_template_kwargs\": {\"enable_thinking\": false}\n  }'\n",[90,740,741,751,761,769,774,779,785,791,797,803,809,815,821,827,833],{"__ignoreMap":25},[659,742,743,746,749],{"class":661,"line":662},[659,744,745],{"class":665},"curl",[659,747,748],{"class":669}," http:\u002F\u002F127.0.0.1:8000\u002Fv1\u002Fchat\u002Fcompletions",[659,750,713],{"class":673},[659,752,753,756,759],{"class":661,"line":683},[659,754,755],{"class":673},"  -H",[659,757,758],{"class":669}," 'Content-Type: application\u002Fjson'",[659,760,713],{"class":673},[659,762,763,766],{"class":661,"line":692},[659,764,765],{"class":673},"  -d",[659,767,768],{"class":669}," '{\n",[659,770,771],{"class":661,"line":699},[659,772,773],{"class":669},"    \"model\": \"qwen38-neo\",\n",[659,775,776],{"class":661,"line":716},[659,777,778],{"class":669},"    \"messages\": [\n",[659,780,782],{"class":661,"line":781},6,[659,783,784],{"class":669},"      {\n",[659,786,788],{"class":661,"line":787},7,[659,789,790],{"class":669},"        \"role\": \"user\",\n",[659,792,794],{"class":661,"line":793},8,[659,795,796],{"class":669},"        \"content\": \"Write a Python function that removes duplicate integers while preserving their original order.\"\n",[659,798,800],{"class":661,"line":799},9,[659,801,802],{"class":669},"      }\n",[659,804,806],{"class":661,"line":805},10,[659,807,808],{"class":669},"    ],\n",[659,810,812],{"class":661,"line":811},11,[659,813,814],{"class":669},"    \"temperature\": 0,\n",[659,816,818],{"class":661,"line":817},12,[659,819,820],{"class":669},"    \"max_tokens\": 256,\n",[659,822,824],{"class":661,"line":823},13,[659,825,826],{"class":669},"    \"stream\": true,\n",[659,828,830],{"class":661,"line":829},14,[659,831,832],{"class":669},"    \"chat_template_kwargs\": {\"enable_thinking\": false}\n",[659,834,836],{"class":661,"line":835},15,[659,837,838],{"class":669},"  }'\n",[10,840,841,842],{},"The example explicitly disables thinking through the retained source template. It is an interactive example, not the fixed 128-output-token benchmark request.",[17,843,844],{},[20,845,27],{"href":22,"ariaDescribedBy":846,"dataFootnoteRef":25,"id":847},[24],"user-content-fnref-readme-5",[82,849,851],{"id":850},"more-useful-performance-without-another-serving-stack","More useful performance, without another serving stack",[10,853,854],{},"The point is not that every model needs a new engine. It is that a useful fine-tune should not need to lose its identity to fit an optimized deployment path.",[10,856,857,858],{},"For this release, the original GGUF and vLLM stay in place. Paiton changes the execution underneath them.",[17,859,860],{},[20,861,41],{"href":38,"ariaDescribedBy":862,"dataFootnoteRef":25,"id":863},[24],"user-content-fnref-native-7",[10,865,866],{},[13,867,868],{},"Keep the fine-tune. Keep vLLM. Get more from the Radeon you already have.",[10,870,871,872,93,876],{},"Start with the public model guide and its benchmark report. For AMD inference workloads beyond this qualified profile, talk to us about ",[20,873,875],{"href":874},"\u002Fproducts\u002Fpaiton","Paiton",[17,877,878],{},[20,879,27],{"href":22,"ariaDescribedBy":880,"dataFootnoteRef":25,"id":881},[24],"user-content-fnref-readme-6",[883,884],"hr",{},[886,887,890,895],"section",{"className":888,"dataFootnotes":25},[889],"footnotes",[82,891,894],{"className":892,"id":24},[893],"sr-only","Footnotes",[896,897,898,952,1007,1021,1034,1048,1061,1074,1087,1100,1148,1175,1216],"ol",{},[899,900,902,908,909,916,917,916,924,916,931,916,938,916,945],"li",{"id":901},"user-content-fn-readme",[20,903,907],{"href":904,"rel":905},"https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin\u002Fblob\u002Fmain\u002Fmodels\u002FQwen3.8-NEO-CODER-MAX\u002FREADME.md",[906],"nofollow","Paiton NEO model guide",". ",[20,910,915],{"href":911,"ariaLabel":912,"className":913,"dataFootnoteBackref":25},"#user-content-fnref-readme","Back to reference 1",[914],"data-footnote-backref","↩"," ",[20,918,915,922],{"href":919,"ariaLabel":920,"className":921,"dataFootnoteBackref":25},"#user-content-fnref-readme-2","Back to reference 1-2",[914],[17,923,41],{},[20,925,915,929],{"href":926,"ariaLabel":927,"className":928,"dataFootnoteBackref":25},"#user-content-fnref-readme-3","Back to reference 1-3",[914],[17,930,52],{},[20,932,915,936],{"href":933,"ariaLabel":934,"className":935,"dataFootnoteBackref":25},"#user-content-fnref-readme-4","Back to reference 1-4",[914],[17,937,60],{},[20,939,915,943],{"href":940,"ariaLabel":941,"className":942,"dataFootnoteBackref":25},"#user-content-fnref-readme-5","Back to reference 1-5",[914],[17,944,101],{},[20,946,915,950],{"href":947,"ariaLabel":948,"className":949,"dataFootnoteBackref":25},"#user-content-fnref-readme-6","Back to reference 1-6",[914],[17,951,112],{},[899,953,955,908,960,916,965,916,972,916,979,916,986,916,993,916,1000],{"id":954},"user-content-fn-native",[20,956,959],{"href":957,"rel":958},"https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin\u002Fblob\u002Fmain\u002Fmodels\u002FQwen3.8-NEO-CODER-MAX\u002FNATIVE_GGUF.md",[906],"Native GGUF through vLLM on AMD RDNA4",[20,961,915],{"href":962,"ariaLabel":963,"className":964,"dataFootnoteBackref":25},"#user-content-fnref-native","Back to reference 2",[914],[20,966,915,970],{"href":967,"ariaLabel":968,"className":969,"dataFootnoteBackref":25},"#user-content-fnref-native-2","Back to reference 2-2",[914],[17,971,41],{},[20,973,915,977],{"href":974,"ariaLabel":975,"className":976,"dataFootnoteBackref":25},"#user-content-fnref-native-3","Back to reference 2-3",[914],[17,978,52],{},[20,980,915,984],{"href":981,"ariaLabel":982,"className":983,"dataFootnoteBackref":25},"#user-content-fnref-native-4","Back to reference 2-4",[914],[17,985,60],{},[20,987,915,991],{"href":988,"ariaLabel":989,"className":990,"dataFootnoteBackref":25},"#user-content-fnref-native-5","Back to reference 2-5",[914],[17,992,101],{},[20,994,915,998],{"href":995,"ariaLabel":996,"className":997,"dataFootnoteBackref":25},"#user-content-fnref-native-6","Back to reference 2-6",[914],[17,999,112],{},[20,1001,915,1005],{"href":1002,"ariaLabel":1003,"className":1004,"dataFootnoteBackref":25},"#user-content-fnref-native-7","Back to reference 2-7",[914],[17,1006,126],{},[899,1008,1010,1015,1016],{"id":1009},"user-content-fn-model",[20,1011,1014],{"href":1012,"rel":1013},"https:\u002F\u002Fhuggingface.co\u002FDavidAU\u002FQwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF",[906],"DavidAU model card",", download snapshot checked September 14, 2026. ",[20,1017,915],{"href":1018,"ariaLabel":1019,"className":1020,"dataFootnoteBackref":25},"#user-content-fnref-model","Back to reference 3",[914],[899,1022,1024,908,1029],{"id":1023},"user-content-fn-downloads",[20,1025,1028],{"href":1026,"rel":1027},"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fmodels-download-stats",[906],"Hugging Face: Models Download Stats",[20,1030,915],{"href":1031,"ariaLabel":1032,"className":1033,"dataFootnoteBackref":25},"#user-content-fnref-downloads","Back to reference 4",[914],[899,1035,1037,1042,1043],{"id":1036},"user-content-fn-vllm",[20,1038,1041],{"href":1039,"rel":1040},"https:\u002F\u002Fdocs.vllm.ai\u002Fen\u002Fstable\u002Ffeatures\u002Fquantization\u002Fgguf\u002F",[906],"vLLM: GGUF documentation",", checked September 14, 2026. ",[20,1044,915],{"href":1045,"ariaLabel":1046,"className":1047,"dataFootnoteBackref":25},"#user-content-fnref-vllm","Back to reference 5",[914],[899,1049,1051,908,1056],{"id":1050},"user-content-fn-upstream",[20,1052,1055],{"href":1053,"rel":1054},"https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fvllm-gguf-plugin",[906],"Upstream vllm-gguf-plugin",[20,1057,915],{"href":1058,"ariaLabel":1059,"className":1060,"dataFootnoteBackref":25},"#user-content-fnref-upstream","Back to reference 6",[914],[899,1062,1064,908,1069],{"id":1063},"user-content-fn-gguf",[20,1065,1068],{"href":1066,"rel":1067},"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fgguf",[906],"Hugging Face: GGUF",[20,1070,915],{"href":1071,"ariaLabel":1072,"className":1073,"dataFootnoteBackref":25},"#user-content-fnref-gguf","Back to reference 7",[914],[899,1075,1077,908,1082],{"id":1076},"user-content-fn-llama",[20,1078,1081],{"href":1079,"rel":1080},"https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp",[906],"llama.cpp",[20,1083,915],{"href":1084,"ariaLabel":1085,"className":1086,"dataFootnoteBackref":25},"#user-content-fnref-llama","Back to reference 8",[914],[899,1088,1090,908,1095],{"id":1089},"user-content-fn-lock",[20,1091,1094],{"href":1092,"rel":1093},"https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin\u002Fblob\u002Fmain\u002Fmodels\u002FQwen3.8-NEO-CODER-MAX\u002Fcheckpoint.lock.json",[906],"Checkpoint lock",[20,1096,915],{"href":1097,"ariaLabel":1098,"className":1099,"dataFootnoteBackref":25},"#user-content-fnref-lock","Back to reference 9",[914],[899,1101,1103,908,1108,916,1113,916,1120,916,1127,916,1134,916,1141],{"id":1102},"user-content-fn-bench",[20,1104,1107],{"href":1105,"rel":1106},"https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin\u002Fblob\u002Fmain\u002Fmodels\u002FQwen3.8-NEO-CODER-MAX\u002FBENCHMARKS.md",[906],"NEO v1.1.0 benchmarks",[20,1109,915],{"href":1110,"ariaLabel":1111,"className":1112,"dataFootnoteBackref":25},"#user-content-fnref-bench","Back to reference 10",[914],[20,1114,915,1118],{"href":1115,"ariaLabel":1116,"className":1117,"dataFootnoteBackref":25},"#user-content-fnref-bench-2","Back to reference 10-2",[914],[17,1119,41],{},[20,1121,915,1125],{"href":1122,"ariaLabel":1123,"className":1124,"dataFootnoteBackref":25},"#user-content-fnref-bench-3","Back to reference 10-3",[914],[17,1126,52],{},[20,1128,915,1132],{"href":1129,"ariaLabel":1130,"className":1131,"dataFootnoteBackref":25},"#user-content-fnref-bench-4","Back to reference 10-4",[914],[17,1133,60],{},[20,1135,915,1139],{"href":1136,"ariaLabel":1137,"className":1138,"dataFootnoteBackref":25},"#user-content-fnref-bench-5","Back to reference 10-5",[914],[17,1140,101],{},[20,1142,915,1146],{"href":1143,"ariaLabel":1144,"className":1145,"dataFootnoteBackref":25},"#user-content-fnref-bench-6","Back to reference 10-6",[914],[17,1147,112],{},[899,1149,1151,908,1156,916,1161,916,1168],{"id":1150},"user-content-fn-image",[20,1152,1155],{"href":1153,"rel":1154},"https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin\u002Fblob\u002Fmain\u002Fmodels\u002FQwen3.8-NEO-CODER-MAX\u002FIMAGE_API.md",[906],"Image API",[20,1157,915],{"href":1158,"ariaLabel":1159,"className":1160,"dataFootnoteBackref":25},"#user-content-fnref-image","Back to reference 11",[914],[20,1162,915,1166],{"href":1163,"ariaLabel":1164,"className":1165,"dataFootnoteBackref":25},"#user-content-fnref-image-2","Back to reference 11-2",[914],[17,1167,41],{},[20,1169,915,1173],{"href":1170,"ariaLabel":1171,"className":1172,"dataFootnoteBackref":25},"#user-content-fnref-image-3","Back to reference 11-3",[914],[17,1174,52],{},[899,1176,1178,908,1183,916,1188,916,1195,916,1202,916,1209],{"id":1177},"user-content-fn-release",[20,1179,1182],{"href":1180,"rel":1181},"https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin\u002Fblob\u002Fmain\u002Fmodels\u002FQwen3.8-NEO-CODER-MAX\u002Fpaiton-release.json",[906],"v1.1.0 release manifest",[20,1184,915],{"href":1185,"ariaLabel":1186,"className":1187,"dataFootnoteBackref":25},"#user-content-fnref-release","Back to reference 12",[914],[20,1189,915,1193],{"href":1190,"ariaLabel":1191,"className":1192,"dataFootnoteBackref":25},"#user-content-fnref-release-2","Back to reference 12-2",[914],[17,1194,41],{},[20,1196,915,1200],{"href":1197,"ariaLabel":1198,"className":1199,"dataFootnoteBackref":25},"#user-content-fnref-release-3","Back to reference 12-3",[914],[17,1201,52],{},[20,1203,915,1207],{"href":1204,"ariaLabel":1205,"className":1206,"dataFootnoteBackref":25},"#user-content-fnref-release-4","Back to reference 12-4",[914],[17,1208,60],{},[20,1210,915,1214],{"href":1211,"ariaLabel":1212,"className":1213,"dataFootnoteBackref":25},"#user-content-fnref-release-5","Back to reference 12-5",[914],[17,1215,101],{},[899,1217,1219,908,1224],{"id":1218},"user-content-fn-publication",[20,1220,1223],{"href":1221,"rel":1222},"https:\u002F\u002Fgithub.com\u002FEliovp-BV\u002Fpaiton-vllm-plugin\u002Fblob\u002Fmain\u002Fmodels\u002FQwen3.8-NEO-CODER-MAX\u002Fpublication-checks.json",[906],"Publication checks",[20,1225,915],{"href":1226,"ariaLabel":1227,"className":1228,"dataFootnoteBackref":25},"#user-content-fnref-publication","Back to reference 13",[914],[1230,1231,1232],"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 pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .szBVR, html code.shiki .szBVR{--shiki-default:#D73A49;--shiki-dark:#F97583}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: 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Explore measured latency gains, image input and local deployment.","md",null,"\u002Fasset\u002Fimages\u002Fblog\u002Fpaiton-neo-gguf\u002F00-hero-neo-gguf-r9700.webp",{},"https:\u002F\u002Feliovp.com\u002Fblog\u002Fpaiton-qwen38-neo-gguf-vllm-r9700","\u002Fblog\u002Fpaiton-qwen38-neo-gguf-vllm-r9700",{"title":5,"description":1252},"paiton-qwen38-neo-gguf-vllm-r9700","blog\u002Fpaiton-qwen38-neo-gguf-vllm-r9700","ewli2UQNcXaYFv--LW_YMMIb0cwvyGkmJe0L2zg5hws",[1264,1276,1278,1290,1300,1315,1324,1338,1369,1381,1403,1421,1440,1458,1476,1493,1505,1521,1536,1548,1557,1565,1580,1592,1603,1614,1624,1637,1647,1660,1671,1681,1692,1701,1713,1724,1733],{"path":1265,"title":1266,"description":1267,"date":1268,"slug":1269,"image":1270,"originalUrl":1271,"categories":1272},"\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. 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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",[1332,1333,1334,1335,1336,1337],"All","AI Infrastructure","Data Centers","ModFlex","HPC","AMD Helios",{"path":1339,"title":1340,"description":1341,"date":1342,"slug":1343,"image":1344,"originalUrl":1345,"categories":1346},"\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",[1332,1309,875,1347,1348,1349,1350,1351,1352,1353,1354,1355,546,1356,1357,1358,1359,1360,1361,1362,875,1363,1364,1365,1366,1367,1368],"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":1370,"title":1371,"description":1372,"date":1373,"slug":1374,"image":1375,"originalUrl":1376,"categories":1377},"\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",[1332,1309,1378,1379,1348,1380,1378,1360],"Modular DC","Uncategorized","De Tijd",{"path":1382,"title":1383,"description":1384,"date":1385,"slug":1386,"image":1387,"originalUrl":1388,"categories":1389},"\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",[1332,1309,1390,1379,1391,1392,1393,1394,1395,1396,1397,1398,1354,1399,1355,1400,1401,1402],"Trending","AI Act","Anthropomorphism","AVG","Benelux","ChatGPT","Cybersecurity","Data Governance","Data Security","GDPR","Microsoft Copilot","Privacy","Shadow AI",{"path":1404,"title":1405,"description":1406,"date":1407,"slug":1408,"image":1409,"originalUrl":1410,"categories":1411},"\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",[1332,1412,1413,1414,1396,1415,1416,1417,1399,1418,1419,1420,1401],"AWS","Belgian Mobile ID","Cloud Act","Data Sovereignty","Digital Identity","eIDAS","itsme","Liberty Global","MyGov.be",{"path":1422,"title":1423,"description":1424,"date":1425,"slug":1426,"image":1427,"originalUrl":1428,"categories":1429},"\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",[1332,1309,1390,1333,1449,1450,1451,1452,1453,1454,1455,1456,1457],"AI Neocloud","Circular Financing","GPU Cloud","Investment Risks","Startup Valuation","Synthetic Bubble","Tech Analysis","Vaporware","Venture Capital",{"path":1459,"title":1460,"description":1461,"date":1462,"slug":1463,"image":1464,"originalUrl":1465,"categories":1466},"\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 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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",[1332,1309,875,1379,1485,1309,1486,1487,1488,1489,1490,1491,875,1492],"AMD MI300X","CUDA Translation","FP8","GPU Optimization","High Performance Computing","HIP","Kernel Tuning","ROCm",{"path":1494,"title":1495,"description":1496,"date":1497,"slug":1498,"image":1499,"originalUrl":1500,"categories":1501},"\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",[1332,1309,875,1310,1502,1485,1314,1503,1274,1491,875,1504,1250],"AMD Instinct","High Throughput","SGLang",{"path":1506,"title":1507,"description":1508,"date":1509,"slug":1510,"image":1511,"originalUrl":1512,"categories":1513},"\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",[1332,1309,875,1514,1485,1515,1274,1516,1517,1518,1519,875,1520],"AI Benchmarks","Cost per Token","Mixture of Experts","MoE","NVIDIA B200","NVIDIA H200","Qwen3",{"path":1522,"title":1523,"description":1524,"date":1525,"slug":1526,"image":1527,"originalUrl":1528,"categories":1529},"\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",[1332,1309,1430,1379,1431,1530,1531,1532,1533,1436,1438,1363,1534,1535],"Damage Detection","Document Processing","Email Automation","Invoice Extraction","Ticket Automation","Workflow Automation",{"path":1537,"title":1538,"description":1539,"date":1540,"slug":1541,"image":1542,"originalUrl":1543,"categories":1544},"\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",[1332,1309,875,1379,1545,1348,1349,1546,1547,1359,1360,875,1250],"AI","H200","MI300X",{"path":1549,"title":1550,"description":1551,"date":1552,"slug":1553,"image":1554,"originalUrl":1555,"categories":1556},"\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",[1332,1309,1430,1431,1530,1531,1532,1533,1436,1438,1363,1534,1535],{"path":1558,"title":1559,"description":1560,"date":1561,"slug":1562,"image":25,"originalUrl":1563,"categories":1564},"\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",[1332,1309,875],{"path":1566,"title":1567,"description":1568,"date":1569,"slug":1570,"image":1571,"originalUrl":1572,"categories":1573},"\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",[1332,1309,875,1379,1310,1485,1574,1487,1575,1576,1577,875,1578,1579],"Cold Start","Graph Compilation","Llama 3.1 405B","LLM Optimization","Startup Latency","Tensor Parallelism",{"path":1581,"title":1582,"description":1583,"date":1584,"slug":1585,"image":1586,"originalUrl":1587,"categories":1588},"\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",[1332,1309,875,1379,1485,1589,1435,1355,1311,1312,1313,1576,1590,1591],"Cold Start Optimization","Model Serving","vLLM Optimization",{"path":1593,"title":1594,"description":1595,"date":1596,"slug":1597,"image":1598,"originalUrl":1599,"categories":1600},"\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",[1332,1309,875,1430,1379,1348,1547,1360,1601,1602],"RX7900XTX","tenstorrent",{"path":1604,"title":1605,"description":1606,"date":1607,"slug":1608,"image":1609,"originalUrl":1610,"categories":1611},"\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 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images.","2025-04-11T14:45:48","examining-ai-agents-in-the-medical-field-ai-that-speaks-dicom","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Fhealthcareblog-1.webp","https:\u002F\u002Feliovp.com\u002Fexamining-ai-agents-in-the-medical-field-ai-that-speaks-dicom\u002F",[1332,1309,1430,1379,1545,1348,1646],"Healthcare",{"path":1648,"title":1649,"description":1650,"date":1651,"slug":1652,"image":1653,"originalUrl":1654,"categories":1655},"\u002Fblog\u002Feliovp-bv-your-trusted-partner-for-supply-chain-resilience-amidst-new-u-s-tariffs","U.S. Tariffs and AI Supply Chain Resilience: April 2025","Read ElioVP's April 2025 perspective on U.S. tariffs and supply chain resilience for AI servers, HPC systems and modular data centers.","2025-04-04T10:01:27","eliovp-bv-your-trusted-partner-for-supply-chain-resilience-amidst-new-u-s-tariffs","\u002Fasset\u002Fimages\u002Fblog\u002Fimported\u002Ftariffsshipping.webp","https:\u002F\u002Feliovp.com\u002Feliovp-bv-your-trusted-partner-for-supply-chain-resilience-amidst-new-u-s-tariffs\u002F",[1332,1390,1545,1348,1656,1657,1658,1659],"import","Taiwan","Tariffs","Trump",{"path":1661,"title":1662,"description":1663,"date":1664,"slug":1665,"image":1666,"originalUrl":1667,"categories":1668},"\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",[1332,1309,1430,1545,1669,1670],"AI 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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",[1332,1309,875,1545,1348,1690,1691,1547,875],"benchmark","LLM",{"path":1693,"title":1694,"description":1695,"date":1696,"slug":1697,"image":1698,"originalUrl":1699,"categories":1700},"\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",[1332,1309,875],{"path":1702,"title":1703,"description":1704,"date":1705,"slug":1706,"image":1707,"originalUrl":1708,"categories":1709},"\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",[1332,1348,1710,1711,1712],"Jim Greene","Podcast","Tech 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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",[1332,1309,875,1348,1723,1546,1547,1612,875,1250],1789853166458]