{"slug": "spec-sheets-are-not-kernels-int8-availability-on-nvidia-blackwell-ultra", "title": "Spec Sheets Are Not Kernels: INT8 Availability on Nvidia Blackwell Ultra", "summary": "An audit by computer science researchers finds that NVIDIA's Blackwell Ultra GPU (B300) nominally supports INT8 tensor-core compute at a 30:1 FP8-to-INT8 ratio, but the format is undeployable by default due to a layered withdrawal across NVIDIA's own stack. The PTX ISA never exposes the fifth-generation integer path on sm_103a, CUTLASS skips INT8 UMMA generation for 103a, and vLLM fails with a hard runtime error on the first forward pass, while SGLang's INT8 GEMM stops at Sm90. The findings show that quantization format availability depends on the entire software stack, not just the spec sheet.", "body_md": "# Computer Science > Hardware Architecture\n\n[Submitted on 12 Aug 2026]\n\n# Title:Spec Sheets Are Not Kernels: An ISA- and Source-Level Audit of INT8 Availability on NVIDIA Blackwell Ultra\n\n[View PDF](/pdf/2608.11693)\n\n[HTML (experimental)](https://arxiv.org/html/2608.11693v1)\n\nAbstract:NVIDIA's published specifications give the Blackwell Ultra GPU (B300) a dense-compute ratio of roughly 30:1 between FP8 and INT8 tensor-core throughput; its predecessors, H200 and B200, both provide 1:1. We audit what this deprioritization means in practice by tracing INT8 W8A8 support through four layers of the stack: the published specifications, the PTX ISA, NVIDIA's CUTLASS kernel library, and the two major open-source LLM serving engines (vLLM and SGLang). We find a consistent, layered withdrawal: (i) the PTX ISA never exposes the fifth-generation tensor-core integer path ([this http URL]with .kind::i8) on sm_103a, even though the same PTX revision extends the FP4 kinds to that target, leaving legacy warp-level IMMA as the only architecturally legal integer tensor-core path on B300; (ii) CUTLASS's kernel generator explicitly skips INT8 UMMA generation for any build targeting 103a, while generating FP8 unconditionally; (iii) vLLM ships no INT8 GEMM for Blackwell and fails with a hard runtime error at the first forward pass, after the model has loaded; and (iv) SGLang's ahead-of-time INT8 GEMM stops at Sm90, while its FP8 tuning configurations already cover B200. We document an escape hatch (rerouting vLLM's INT8 path to a JIT-compiled Triton backend via an environment variable), a false-negative trap in the obvious profiler methodology for detecting \"native INT8\" on sm_103, and the practical failure semantics that make naive testing expensive. Together, these findings show that a quantization format's availability is a property of the whole stack rather than of the model or the spec sheet. Four distinct layers, three of them NVIDIA's own, withdrew INT8 support in mutually consistent ways, and a format that is nominally present on the datasheet is, by default, undeployable on this hardware.\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/spec-sheets-are-not-kernels-int8-availability-on-nvidia-blackwell-ultra", "canonical_source": "https://arxiv.org/abs/2608.11693", "published_at": "2026-08-13 13:54:25+00:00", "updated_at": "2026-08-13 14:12:58.491865+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-infrastructure"], "entities": ["NVIDIA", "Blackwell Ultra", "B300", "H200", "B200", "CUTLASS", "vLLM", "SGLang"], "alternates": {"html": "https://wpnews.pro/news/spec-sheets-are-not-kernels-int8-availability-on-nvidia-blackwell-ultra", "markdown": "https://wpnews.pro/news/spec-sheets-are-not-kernels-int8-availability-on-nvidia-blackwell-ultra.md", "text": "https://wpnews.pro/news/spec-sheets-are-not-kernels-int8-availability-on-nvidia-blackwell-ultra.txt", "jsonld": "https://wpnews.pro/news/spec-sheets-are-not-kernels-int8-availability-on-nvidia-blackwell-ultra.jsonld"}}