Unfolding the Leech Lattice: Fused Multi-Shell Decoding and VRAM Layouts for 2-Bit LLM Weights
cs.LG
Submitted: 2026-09-02
Updated: 2026-09-02
Comments: 21 pages, 5 figures. Preprint, not peer reviewed. Also deposited at Zenodo, doi:10.5281/zenodo.22133606
Code: https://github.com/ggml-org/llama.cpp
License: http://creativecommons.org/licenses/by/4.0/
The gist: Leech-lattice vector quantization holds the strongest reported 2-bit quality under its own evaluation protocol.
Terminology
Abstract
Leech-lattice vector quantization holds the strongest reported 2-bit quality under its own evaluation protocol. Its kernel decodes one shell; we found no implementation of the multi-shell decoder the rate requires. This paper supplies one and measures its serving cost for decode-phase GEMV at batch 1. First, a serving path for the full 301-class codebook: an offline expansion into GPU layouts and a fused dequantize-plus-matvec kernel reading them without warp divergence, verified against f64. Second, the in-VRAM rate is a design axis distinct from the on-disk rate. Four bit-exact layouts timed in one process show binary bit planes beating one-hot masks on size and speed at constant bandwidth (4.80 bits per weight, 2.15x FP16). Below 4.3 bits a second, irregular stream enters; at 3.6 the decode stops being shifts and masks. Third, deployed four-bit (AWQ) and two-bit (QTIP) GEMV kernels run in the same process. The trellis kernel reads 2.40x fewer bytes than our served layout and runs 2.27x faster at near-equal fractions of their byte bounds: the time gap tracks the traffic gap, the price of unfolding a codebook too large for a lookup table. Fourth, the validity envelope: the trellis kernel outruns our no-weights control, so our launch geometry sets that floor, and on a second memory hierarchy every lattice arm falls below FP16. With the output head held identical across arms, the kernel-and-format path gains 1.11x, 1.29x and 1.41x end to end at 4B, 8B and 14B; with an int8 output head the served 4B reaches 87.0 tok/s in 2.60 GB. The quality cost, 1.38x perplexity and 14.7 MMLU points at 4B, shrinks across the three sizes measured.
Sources
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- QuIP: 2-Bit Quantization of Large Language Models With Guarantees
- SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
- Extreme Compression of Large Language Models via Additive Quantization
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- Measuring Massive Multitask Language Understanding
- SqueezeLLM: Dense-and-Sparse Quantization
- Efficient Memory Management for Large Language Model Serving with PagedAttention
- AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
- VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models
- SpinQuant: LLM quantization with learned rotations
- Pointer Sentinel Mixture Models
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- Qwen3 Technical Report
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
- QTIP: Quantization with Trellises and Incoherence Processing
- GPTVQ: The Blessing of Dimensionality for LLM Quantization
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