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: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.
arXiv:2608. 10010v2 Announce Type: replace Abstract: Low-precision formats usually optimize scalar fidelity while inheriting conventional product arithmetic.
arXiv:2608. 10010v1 Announce Type: new Abstract: Low-precision datatypes reduce language-model cost, but most formats optimize scalar fidelity while leaving the arithmetic induced by their products unchanged.
arXiv:2605. 08565v2 Announce Type: replace Abstract: Microscaling is a critical technique for preserving the quality of Large Language Models (LLMs) quantized to ultra-low precision formats.
arXiv:2608. 13756v1 Announce Type: new Abstract: Two GPU kernels implementing the same scaled INT8 GEMM interface are usually treated as interchangeable.
arXiv:2606. 04028v1 Announce Type: new Abstract: The IEEE P3109 draft standard defines a parameterized family of binary floating-point formats and associated operations, with a focus on facilitating machine learning.
arXiv:2609.16338v1 Announce Type: new Abstract: Ternary Large Language Models (LLM) store every weight as one of three symbols $\{-1,0,+1\}$, so the cost of a ternary model is conventionally referenc...
The paper introduces Numbat, a self‑contained machine‑learning stack implemented entirely in Zig with no external runtime dependencies. It covers tensor computation, automatic differentiation, neural‑network modules, mixed precision, multi‑GPU training, data loading, and monitoring, and exposes a stable C ABI with over 1,400 entry points and bindings for six languages. The authors verify the stack against a reference implementation at multiple levels, uncovering ten silent recipe divergences, and demonstrate its practical capability by training a 25.9M‑parameter YOLOv8m detector on COCO 2017, achieving a competitive mAP score and matching single‑GPU performance.
arXiv:2606. 06510v2 Announce Type: replace-cross Abstract: Conventional HPC holds that native hardware FP64 is the irreducible foundation of scientific computing.
The paper introduces format‑aware fusion, a method that co‑designs quantization producers with their scale domains and consumer layouts to fully exploit four‑bit floating‑point (FP4) Tensor Cores. Using this approach, the authors pretrain the Llama‑3‑family 8B model on 160 billion tokens, achieving up to 37.9 K tokens/s/GPU—significantly higher than standard bfloat16 or Transformer Engine FP4 baselines. The study demonstrates that FP4 performance depends on the interplay of scaling, operand packing, layout, and execution path, with downstream task rankings diverging from training‑loss rankings.
arXiv:2606. 20128v1 Announce Type: cross Abstract: Benchmarks for LLM-generated GPU kernels (KernelBench, TritonBench, GEAK) score correctness through fixed-shape, small-sample allclose-style checks.
The paper evaluates the effectiveness of tolerance‑based conformance tests for INT8 quantized GEMM kernels used in large language models. By injecting nine faults into a Qwen3‑1.7B reference pipeline, the authors show that most faults shift outputs by at most one bfloat16 spacing, rendering a tolerance of one spacing blind to these errors. They further demonstrate that requantizing weight scales to the nearest power of two aligns CUTLASS and Triton implementations bit‑for‑bit and produces identical token sequences, with only minor perplexity changes.