DeML OS - 2026-02-08

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Explore Frontier

02.08

2026

Sun

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Paper

Mugi: Value Level Parallelism For Efficient LLMs https://arxiv.org/abs/2601.10823

Daniel Price VLP GEMM

In the paper, Price et al. study VLP for LLMs, extending it to nonlinear ops and small-batch GEMMs, and propose the Mugi architecture to improve throughput, energy efficiency, and sustainability.

Notes

Extends VLP beyond symmetric GEMM to nonlinear LLM operations.
Uses value-centric approximations to preserve important values.
Achieves better end-to-end LLM accuracy and performance.
Optimizes VLP for small-batch and asymmetric GEMMs.
Integrates weight-only and KV cache quantization with GQA.
Mugi improves throughput, energy efficiency, and reduces carbon footprint.

Collected by @icerdesign

DeML OS Q & A 问答

Deep Dive 💬

02.08

2026

Sun

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What is Value Level Parallelism (VLP), and what problem was it originally designed to solve?

VLP is a parallelization technique that exploits value distributions. It was originally proposed to accelerate low-precision, large-batch GEMMs by assigning different accuracy or compute paths to values of different importance.

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Why are small-batch, asymmetric GEMMs challenging for VLP?

Classic VLP assumes symmetric inputs and large batches to amortize overhead. LLM inference often uses small batches with weight-only and KV-cache quantization, breaking these assumptions and requiring new designs.

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How does the Mugi architecture support multiple LLM optimizations without sacrificing generality?

Mugi abstracts VLP into a unified value-level execution framework, making weight quantization, KV-cache quantization, and GQA composable strategies rather than fixed paths, enabling full Transformer support.

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Prompted by @icerdesign