Search papers, labs, and topics across Lattice.
This paper conducts a comprehensive audit of INT8 availability on NVIDIA's Blackwell Ultra GPU by analyzing specifications, the PTX ISA, the CUTLASS kernel library, and major open-source LLM serving engines. The findings reveal a systematic withdrawal of INT8 support across multiple layers, with the PTX ISA failing to expose critical tensor-core paths and CUTLASS skipping INT8 UMMA generation entirely. Ultimately, the research highlights that the availability of a quantization format like INT8 is contingent on the entire software stack, not just the hardware specifications, leading to significant implications for deployment and performance in AI applications.
INT8 support on NVIDIA's Blackwell Ultra GPU is effectively non-existent despite being listed in specifications, revealing a critical gap between hardware promises and practical usability.
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 (tcgen05.mma 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.
