arXiv Machine Learning
What You Sign Is Not What You Run: The Verifiability Gap in Quantized LLM Supply Chains
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arXiv Machine Learning
Sep 16
Is INT8 Portable? A Cross-Platform Measurement Study of Quantized Inference on Embedded and Automotive Accelerators
The study evaluates the portability of INT8 post‑training quantization across seven hardware platforms, including CPUs, GPUs, and vendor NPUs, by keeping the ONNX model and quantization scales constant. It finds that INT8 performance and output consistency vary significantly: CPU dot‑product instructions determine speedup, identical INT8 outputs only occur when integer kernels match, and vendor NPUs require their own quantization pipelines. The authors also show that edge‑NPU latency is dominated by data transfer rather than compute and provide scripts and reports for reproducibility.
arXiv Machine Learning
Aug 17
The Integer Alibi: Localizing Cross-Kernel Divergence in INT8-Quantized LLM Inference
arXiv:2608. 13756v1 Announce Type: new Abstract: Two GPU kernels implementing the same scaled INT8 GEMM interface are usually treated as interchangeable.
arXiv Machine Learning
Oct 2
QATFactory: A Versatile, Deployment-Aligned Framework for Quantization-aware Training and Distillation of LLMs
arXiv:2609.39223v2 Announce Type: new Abstract: Large language model (LLM) inference is increasingly moving toward lower precision to realize the throughput of hardware accelerators, but aggressive p...
arXiv Machine Learning
Jun 19
FloatDoor: Platform-Triggered Backdoors in LLMs
arXiv:2606. 19535v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in sensitive settings such as software engineering, where their outputs directly shape downstream artifacts.
arXiv AI
Sep 7
Golden Ruler: A Numeric Format Catalog with Bit-Exact Conformance Vectors for FP8, BF16, MXFP4, and Microscaling Formats
The paper introduces the Golden Ruler, a catalog of 109 numeric formats for machine learning hardware, including FP8, BF16, MXFP4, and microscaling block formats. It provides six bit‑exact conformance packs that map to IEEE P3109 v3.2.0 standards, each as a self‑contained JSON document with a SHA‑256 fingerprint and an anchor vector for cross‑pack sanity checks. The work cross‑validates these packs against ml_dtypes 0.5.4 and documents any divergences as spec‑permitted gaps, offering a vendor‑neutral reference for engineers.
arXiv AI
Jun 9
An 84-Format Numeric Catalog with Bit-Exact Conformance Vectors: A Vendor-Neutral Reference for FP8, BF16, MXFP4, and Microscaling Formats
arXiv:2606. 09686v1 Announce Type: cross Abstract: Numeric format proliferation in machine learning hardware -- FP8 (E4M3 and E5M2), BF16, MXFP4, microscaling block formats, and dozens of research variants -- has outpaced the availability of vendor-neutral, bit-exact reference material.