Deterministic LLM Inference Across GPU Kernels: Power-of-Two INT8 Quantization Scales and the Limits of Tolerance-Based Conformance
Not on the map. Why?
Abstract (arXiv)
Conformance suites for quantized GEMM kernels ask whether two implementations agree within a tolerance. We measure what such a suite can detect. Injecting nine faults into a reference INT8 pipeline over 8,232 layer--fault--regime cells of Qwen3-1.7B, we find that every one of five epilogue faults -- scale precision, double rounding, multiplication order, output truncation, fused ordering -- moves the output by at most a single bfloat16 spacing, and by exactly one whenever it moves it at all, across 5,880 cells. A tolerance of one spacing is therefore blind to the entire class by construction: four of the five faults are detected by no check in the suite, and the fifth only under power-of-two scales. Faults that violate the accumulator's exactness preconditions, or that break operand sharing, are detected without exception, and a null fault never fires. What a tolerance-based suite of this shape establishes is therefore narrower than interchangeability: that the preconditions hold, that operands are shared, and that differences stay within one spacing. The power-of-two constraint that exposes the one detected fault is also deployable. Requantizing every weight scale to its nearest power of two makes CUTLASS and Triton agree bitwise at every linear layer (196/196 and 252/252, against 8/196 and 10/252 under the checkpoints' own scales) and yields byte-identical generated token sequences at 1.7B, 8B and 14B (8/8 prompts, against 0/8 at all three). Observed perplexity point estimates are +0.32%, -0.28% and +0.48%; the 90% intervals cover zero at the two smaller sizes but not at 14B, reaching +0.71% and +0.76%. A previously reported +157% perplexity for this intervention was an artifact of a probe that rewrote scales without requantizing the weights; separating the effects attributes 99.8% of it to the resulting weight--scale mismatch rather than to the power-of-two constraint itself.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 27 references that are in the map. First posted on arXiv 25 Aug 2026.
Builds on
13 of 27 references.
References · 13
- 2025Qwen3 Technical Reportcited by 8,820
- 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,593
- 2020Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluationcited by 188
- 2025Understanding and Mitigating Numerical Sources of Nondeterminism in LLM Inferencecited by 53
- 2022RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantizationcited by 15
- 2025Deterministic Inference across Tensor Parallel Sizes That Eliminates Training-Inference Mismatchcited by 14
- 2023Pre-registration for Predictive Modelingcited by 13
- 2023A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidancecited by 12
- 2025Equivalence Checking of ML GPU Kernelscited by 7
- 2025An SMT Formalization of Mixed-Precision Matrix Multiplication: Modeling Three Generations of Tensor Corescited by 5
- 2023DGEMM on Integer Matrix Multiplication Unitcited by 4
- 2025DiFR: Inference Verification Despite Nondeterminismcited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2026LLM-42: Enabling Determinism in LLM Inference with Verified Speculation
- 2025Pushing the Envelope of LLM Inference with Ultra-Low-Bit Quantized Models
- 2025CodeGEMM: A Codebook-Centric Approach to Efficient GEMM in Quantized LLMs
- 2025LiquidGEMM: Hardware-Efficient W4A8 GEMM Kernel for High-Performance LLM Serving
- 2026Syndrome Decoding for Silent Data Corruption in Quantized Integer GPU Arithmetic
- 2026Budget-Aware Compression Pipeline for Single-GPU LLM Inference: Methods, Trade-offs, and Coupling Effects
- 2026PolyQ: Codesigning End-to-End Quantization Framework for Scalable Edge CPU LLM Inference
- 2026Dissecting GPU Utilization for LLM Inference on Nvidia Hopper