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arXiv Machine Learning

Unfolding the Leech Lattice: Fused Multi-Shell Decoding and VRAM Layouts for 2-Bit LLM Weights

The paper introduces a multi‑shell decoder for Leech‑lattice vector quantization, achieving the best reported 2‑bit quality under its evaluation protocol. It presents a GPU‑friendly layout that fuses dequantization with matrix‑vector multiplication, demonstrating significant speed and memory advantages over traditional one‑hot masks and other 4‑bit methods. Experiments show the new kernel outperforms baseline approaches across multiple model sizes, with measurable gains in throughput and reduced byte traffic.

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Tetra: Serving Leech-Lattice Quantized LLMs at 2.7 Bits per Parameter

Tetra introduces a new Leech‑lattice based codebook that reduces the memory footprint of quantized LLMs to about 2.15 bits per weight, enabling efficient 2‑bit quantization without a massive lookup table. The method employs a 64‑state Golay trellis and a shared 16 KiB table, decoding each 24‑weight block with only six table loads and two small lookups. When applied to Qwen3 models (4B, 8B, 14B), Tetra achieves 2.70–2.73 bits per parameter, scoring 63–75 on MMLU and generating 57–114 tokens per second, while maintaining close performance to 4‑bit AWQ and outperforming llama.cpp’s IQ2_XXS on 4B.

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arXiv Machine Learning
Oct 19

Memory Bandwidth, Not FLOPs: Profiling and Accelerating Query-Based Segmentation Decoders on Edge GPUs

The paper shows that query‑based segmentation decoders on edge GPUs are limited by memory bandwidth rather than FLOPs. Profiling reveals that non‑tensor‑core operations consume a large fraction of decoder time and bytes, saturating the DRAM bus. CompactInst mitigates this by using parameter‑free spatial pooling and a lightweight refinement head, achieving significant speedups on Jetson Orin Nano while maintaining accuracy.

By Oliver Busemann (Osnabr\"uck University, CLAAS E-Systems GmbH), Torben T\"oniges (CLAAS E-Systems GmbH), Martin Atzmueller (Semantic Information Systems Group, Osnabr\"uck University, German Research Center for Artificial Intelligence)
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arXiv Machine Learning
Aug 11

RotaryQuant: Fitting 120B MoE Models on Consumer Hardware via Fused Compressed-Space Attention

arXiv:2608. 08081v1 Announce Type: cross Abstract: Large mixture-of-experts (MoE) language models with 26--120 billion parameters exceed the memory capacity of consumer devices through three simultaneous pressures: resident weight matrices, key-value (KV) cache state that grows linearly with context, and dozens of expert sublayers that must be paged on demand.

By Anthony. Lui, Mohamed. Elsaied, N. P. Savani
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arXiv AI
Oct 9

The Missing Fourth Term for the Emulation Tensor Memory Equilibrium (TME) Model: The Residue Deconstruction Cost

The paper identifies a missing cost term in the Tensor‑Memory Equilibrium (TME) model for emulating fp64 on GPUs: the per‑input deconstruction cost required before matrix multiplication can proceed. By adding this term and calibrating it against cuBLAS, the authors derive a closed‑form operational‑intensity threshold (OI*) below which emulation cannot match native fp64 performance. On an NVIDIA B300 GPU, this threshold is about 0.56 FLOP/B, limiting the speed‑ups of memory‑bound kernels such as GEMV, SpMV, and low‑batch GEMV to 0.3–0.9× native, and reducing the 7‑point stencil speed‑up from 3.1× to 1.8×, while dense GEMM remains unaffected.

By Harun Bayraktar, John Gunnels, Peter Caday
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