VQV Signal
Testing two GPU kernels (CUTLASS vs Triton) for INT8 GEMM in LLM inference reveals bit-for-bit reproducibility within each kernel but no agreement between them on output sequences. This challenges the assumption that such kernels are interchangeable under fixed conditions.
Testing two GPU kernels (CUTLASS vs Triton) for INT8 GEMM in LLM inference reveals bit-for-bit reproducibility within each kernel but no agreement between them on output sequences. This challenges the assumption that such kernels are interchangeable under fixed conditions.
AI-assisted summary based on the listed source.
Two GPU kernels implementing the same scaled INT8 GEMM interface are usually treated as interchangeable. We test that assumption: holding the checkpoint, prompts, hardware, inference engine, decoding, and quantization configuration fixed, we swap only the INT8 linear kernel (CUTLASS versus Triton) inside vLLM. At...
This finding highlights that kernel choice can significantly affect LLM inference outputs even when all other factors are held constant, impacting reproducibility and reliability in quantized model deployments.
Hardware and robotics watchers may want to track whether this becomes a product, benchmark, or deployment signal.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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