MX+: Pushing the Limits of Microscaling Formats for Efficient Large Language Model Serving

Review Form

Reviewer: The Guardian (Adversarial Skeptic)

Summary

The authors identify that low-bit microscaling (MX) formats, particularly MXFP4, suffer significant performance degradation in LLMs due to large quantization errors on outlier activation values. They propose MX+, an extension to the MX standard, which repurposes the exponent bits of the largest value in a block (the Block Max, or BM) to serve as additional mantissa bits, thereby increasing its precision. The location of this BM is stored using an additional 8-bit index per block. The authors evaluate this proposal via software emulation, a proposed software implementation using an extra matrix multiplication, and a new hardware design, claiming significant accuracy improvements with negligible overhead.

Strengths

1. Clear Problem Motivation: The paper provides a convincing analysis in Section 3 (pages 2-3) that pinpoints the source of performance degradation in low-bit MX formats. The investigation in Figures 3 and 4 correctly identifies that activation outliers (BMs) are the primary cause of high quantization error, both for the outliers themselves and for the other elements in the block (NBMs) that are scaled by them.

2. Intuitive Core Mechanism: The central insight—that the BM element's exponent is implicitly known and its bits can be repurposed for precision—is a simple and clever observation. This forms a logical basis for the proposed format extension.

3. Broad Empirical Evaluation: The authors have evaluated their proposal across a wide range of modern LLMs (OPT, Llama, Mistral, etc.) and on various academic benchmarks (perplexity and zero-shot tasks), lending some weight to their accuracy claims.

Weaknesses

My primary concerns with this manuscript center on logical inconsistencies between claims and evidence, questionable methodological choices in comparative analysis, and an underestimation of the proposed overheads.

1. Contradictory Claims of "Non-Intrusive" and "Negligible Slowdown": The abstract claims MX+ is a "non-intrusive extension" with "negligible slowdown." The evidence presented contradicts this directly.

   • The proposed software implementation (Section 5.2, page 6) requires decomposing the BM and executing a second, sparse MMA operation. Figure 11 (page 10) shows this incurs a 1.54x slowdown in the prefill stage. This is not a "negligible" overhead.
   • The proposed hardware implementation (Section 6, page 7) requires adding a "BM Detector," "Forward and Swap Unit," and "BM Compute Unit" to the Tensor Core's Dot Product Engine (DPE). Modifying the internal pipeline of a highly optimized unit like a Tensor Core is, by definition, an intrusive architectural change, not a "non-intrusive extension."

2. Unconvincing Hardware Overhead Analysis: The hardware analysis in Section 7.4 and Table 5 (page 10) is based on a 28nm technology node. This is a severely outdated process for evaluating hardware intended for state-of-the-art accelerators, which utilize 4nm or 5nm nodes. Area and power do not scale linearly between such disparate nodes. The authors' assertion that the overhead "would be even smaller if fabricated using more advanced node" is an unsubstantiated claim, not a rigorous analysis. This choice of process node appears designed to minimize the reported overhead figures.

3. Potential for Unfair Baseline Comparisons: In Section 8.1 and Table 7 (page 11), the authors compare MX+ against several other quantization schemes. For ANT, OliVe, and Tender, they create their own variants ("MX-ANT", "MX-OliVe", "MX-Tender") to support "finer-grained grouping." This raises a significant red flag. It is unclear if these re-implementations are faithful to the original work or are unoptimized strawman versions that serve to inflate the relative performance of MX+. Without a detailed description of this re-implementation and validation against the original papers' results, the integrity of this comparison is questionable.

4. Downplayed Storage and Bandwidth Overhead: The MX+ format requires an additional 8 bits per 32-element block to store the BM index. For MXFP4, a block is 32 elements * 4 bits/element = 128 bits. The overhead is therefore 8 / 128 = 6.25%. In the memory-bandwidth-bound decode phase of LLM inference, a 6.25% increase in data movement from memory is not "negligible" and will directly impact latency and energy consumption. The authors fail to quantify the performance impact of this additional bandwidth pressure.

5. Limited Scope of the Core Idea: The authors suggest applicability to non-FP formats like MXINT8 and other industry formats like NVFP4 (Section 8.2, page 12). However, the results for MXINT8+ in Table 10 show almost no benefit, and the proposed extension for NVFP4 is speculative and acknowledges it fails for blocks with small-magnitude values. This suggests the technique is a point solution for a narrow class of FP-based block formats rather than a broadly applicable principle.

Questions to Address In Rebuttal

1. Please reconcile the claims of "non-intrusive" and "negligible slowdown" with the evidence of a 1.54x prefill slowdown in the software implementation and the required modifications to the Tensor Core datapath in the hardware proposal. Which claim is correct?

2. Please provide a principled justification for using a 28nm process for the hardware overhead analysis. Can you provide a more rigorous projection of the area and power overhead on a contemporary 4nm process, accounting for differential scaling of logic and memory, as well as leakage?

3. Please provide evidence that your "MX-" implementations of ANT, OliVe, and Tender (Table 7) are fair, optimized, and faithful comparisons to the original published works. What steps were taken to ensure you were not comparing against weakened baselines?

4. Please provide an empirical analysis of the performance impact (latency, energy) of the 6.25% bandwidth overhead from the BM indices, especially in decode-bound scenarios with long output sequences. On what grounds is this overhead considered "negligible"?

5. The performance improvement of MXFP4++ over MXFP4+ appears marginal in many perplexity results (e.g., Table 3, Llama-3.1-8B, Mistral-7B). Does the added complexity of managing a second, decoupled scale factor for NBMs justify this minor gain?

Review Form

Reviewer: The Synthesizer (Contextual Analyst)

Summary

This paper introduces MX+, a non-intrusive extension to the emerging industry-standard Microscaling (MX) data formats, designed to improve the performance of Large Language Models (LLMs) under ultra-low-bit quantization. The authors correctly identify that the primary obstacle to aggressive 4-bit quantization (specifically, for activations) is the presence of high-magnitude outliers, which cause significant quantization error for both themselves and the other values within their block.

The core contribution is an elegant, format-level solution to this problem. The authors leverage the insight that in a block floating-point (BFP) format like MX, the largest value in a block (the "Block Max" or BM) implicitly has its exponent set to the maximum representable value. Therefore, its explicit exponent field is redundant. MX+ repurposes this redundant exponent field as an extended mantissa, affording the outlier element significantly higher precision without changing its bit-width. This simple change dramatically improves model accuracy for 4-bit formats with negligible storage and computational overhead, making W4A4 (4-bit weights and activations) inference far more viable. The authors support their proposal with a comprehensive evaluation, including software emulation, a detailed hardware implementation proposal for GPU Tensor Cores, and comparisons against a wide array of existing quantization schemes.

Strengths

1. High-Impact Problem and Timely Contribution: The work is situated squarely at the epicenter of a critical challenge in ML systems: enabling efficient 4-bit inference for LLMs. More importantly, by building directly upon the OCP Microscaling (MX) standard [47], the paper ensures its immediate relevance to the direction the industry is already heading. It is not an academic exercise in a vacuum but a direct and practical response to the limitations of an emerging standard.

2. Elegant and Pragmatic Solution: The core idea of repurposing the BM's exponent field is exceptionally clever in its simplicity. Unlike many outlier-mitigation techniques that require complex and often data-dependent pre-processing (e.g., SmoothQuant [63], QuaRot [3]), MX+ is a self-contained, format-level fix. This pragmatism is its greatest strength; it minimizes changes to the software stack and proposes a highly plausible, low-overhead hardware modification (Section 6, page 7). This makes the barrier to real-world adoption remarkably low.

3. Comprehensive Contextualization and Evaluation: The authors have done an excellent job placing their work within the broader landscape. The analysis in Section 2 (page 2) provides a clear overview of industry-driven BFP variants. The empirical evaluation is thorough, spanning multiple models and scales, and the comparisons in Section 8 (page 11) against a host of other academic and industry proposals (Atom, Olive, Tender, etc.) are invaluable for understanding where MX+ fits. The demonstration of synergy with an orthogonal method like Activation-aware Weight Quantization (AWQ) in Table 8 (page 11) is particularly insightful and showcases a deep understanding of the field.

Weaknesses

1. Novelty is in the Solution, Not the Problem: The paper's primary novelty lies in the elegance of its solution. The underlying problem—that outliers in activations are the bane of low-bit quantization—is widely known and has been the subject of intense research for several years. The paper would benefit from making this distinction clearer; its contribution is a powerful new tool in the fight against outliers, rather than the discovery of the fight itself.

2. Understated Philosophical Distinction: The paper compares MX+ to many algorithmic quantization schemes but could more forcefully articulate the fundamental difference in approach. MX+ is a format-level solution, while techniques like SmoothQuant are algorithm-level data transformations. These are not necessarily mutually exclusive. The paper demonstrates this empirically with AWQ, but a more explicit discussion of this format-vs-algorithm dichotomy could strengthen the paper's positioning and help readers understand the unique niche MX+ occupies.

3. The "Multiple Outliers" Analysis Feels Incomplete: The analysis in Section 8.3 (page 12) on addressing multiple outliers per block is a welcome addition, but it feels somewhat brief. The conclusion that handling the top-1 or top-2 outliers provides the most benefit is a significant finding. This could be elevated from a secondary analysis to a more central point about the inherent trade-offs of this solution class. It suggests that while the MX+ approach is highly effective, there may be a fundamental "accuracy ceiling" that can only be surpassed by combining it with the aforementioned algorithmic techniques.

Questions to Address In Rebuttal

1. On Synergy with Algorithmic Pre-processing: The paper demonstrates a successful combination of MX+ with AWQ for weights. Could the authors elaborate on the potential synergy between MX+ for activations and algorithm-level pre-processing techniques like SmoothQuant? For example, could one first apply a light-touch version of SmoothQuant to reduce the magnitude of the most extreme outliers, and then use MXFP4+ to more faithfully represent the now-smaller (but still significant) outliers? Does this combination offer a better accuracy-complexity trade-off?

2. The "Sweet Spot" of Complexity: The analysis in Section 8.3 indicates that focusing on the single largest element (BM) provides the best return on investment. Could the authors expand on why they believe this is the case? Is it because in most LLM activations, there is typically one dominant outlier per block, or is the complexity of tracking and encoding a second outlier (e.g., requiring more index bits, more complex hardware logic) simply not worth the marginal accuracy gain? This would help solidify the design choice of MX+.

3. Path to Real-World Adoption: The proposed hardware extension in Section 6 is clean and appears to have low overhead. Beyond technical feasibility, what do the authors see as the main non-technical hurdles to the adoption of MX+ into the official OCP MX specification or into proprietary hardware like NVIDIA's Tensor Cores? Does the added instruction complexity or the need to manage the BM index metadata present any unforeseen challenges for compiler developers or system architects?

Review Form

Reviewer: The Innovator (Novelty Specialist)

Summary

The authors propose MX+, a non-intrusive extension to the industry-standard Microscaling (MX) data formats, designed to improve the accuracy of low-bit large language model inference. The core claim of novelty rests on a specific insight into block floating-point (BFP) representations like MXFP4: the largest magnitude element in a block, the "Block Max" (BM), which determines the shared block-wide exponent, will itself always be quantized using the maximum representable private exponent for its data type. The authors identify that the exponent bits for this specific BM element are therefore redundant. The proposed contribution, MX+, repurposes these redundant exponent bits as additional mantissa bits for the BM element only. This increases the precision of the most significant outlier in the block without changing the element bit-width or requiring different compute paths for other elements. The authors present both software emulation results demonstrating significant accuracy improvements and a hardware design for integrating MX+ into a GPU Tensor Core.

Strengths

1. A Genuinely Novel Representational Trick: The central idea of identifying and repurposing the redundant private exponent bits of the Block Max element in an MXFP-style format appears to be a novel contribution. While the problem of handling outliers in quantization is extremely well-trodden, the mechanism proposed here is unique. Prior art typically attacks this problem by either (a) pre-processing tensors to reduce outlier magnitude (e.g., SmoothQuant, QuaRot), or (b) using explicit mixed-precision formats where outliers are stored and computed using a different data type entirely (e.g., INT8 for outliers, INT4 for others, as in Atom or Olive). MX+ is distinct because it achieves higher precision for the outlier within the same uniform low-bit data stream. This is an elegant and clever format-level optimization.

2. Pragmatic Grounding in an Existing Standard: The contribution is not an entirely new format created in a vacuum; it is a direct and backward-compatible extension of the OCP Microscaling formats. This grounding in an emerging industry standard makes the idea more than a mere academic curiosity. The non-intrusive nature—maintaining a fixed bit-width per element—is a significant strength over schemes that introduce unaligned memory access patterns.

3. Clear Identification of the Enabling Condition: The authors correctly identify that this trick is specifically enabled by the architecture of the MXFP formats, which feature both a shared block-level exponent and a per-element private exponent. Simpler BFP formats like MSFP (Section 2, page 2), which lack a per-element exponent field, would not permit this specific form of bit repurposing. This demonstrates a clear understanding of the design space and pinpoints the exact source of the exploitable redundancy.

Weaknesses

1. Narrowly Scoped Novelty: While the core mechanism is novel, its applicability is inherently limited to a specific subclass of BFP formats. The contribution is an incremental, albeit very clever, optimization on a pre-existing format, rather than a fundamentally new framework for quantization. The paper's novelty is thus contingent on the existence and adoption of the MXFP format itself. This is not a critique of the idea's value, but an observation on the scope of its conceptual advancement.

2. "Non-Intrusive" Claim Understates Hardware Complexity: The authors repeatedly use the term "non-intrusive." While this holds true from a software and memory layout perspective (uniform bit-width), the proposed hardware implementation in Section 6 (page 7) is decidedly intrusive to the Tensor Core's Dot Product Engine (DPE). The design adds a BM Detector, a Forward and Swap Unit (FSU), and a dedicated BM Compute Unit (BCU). These are non-trivial additions to what is typically a highly optimized, rigid datapath. This represents a significant deviation from a standard DPE and incurs area, power, and, critically, design validation costs. The novelty here is in the format, but the proposed hardware to support it is a specialized datapath modification, the complexity of which is somewhat downplayed.

3. The Underlying Problem is Not New: The paper addresses the age-old problem of outliers. The novelty is in the solution's mechanism, not in the problem definition or the high-level strategy ("give more precision to outliers"). The paper's framing could more sharply distinguish its method from the well-established goal shared by dozens of other papers in this area.

Questions to Address In Rebuttal

1. On Hardware Novelty vs. Complexity: The term "non-intrusive" seems to conflict with the hardware design presented in Section 6. The proposed BM Detector, FSU, and BCU represent a specialized architectural modification. Can the authors justify this claim more rigorously? Specifically, how does the complexity and novelty of this hardware modification compare to the alternative of implementing dual-path MAC units (e.g., for INT4/INT8) as seen in prior hardware-centric outlier-aware works?

2. On Prior Art of Bit Repurposing: The core idea is repurposing redundant bits in a numerical format for an alternative semantic meaning (extended precision). While its application to BFP outliers seems new, have the authors conducted a broader search for similar bit-repurposing concepts in other numerical formats or computer arithmetic contexts, beyond the standard use for signaling NaNs or subnormals? Please clarify if this specific type of representational optimization has any precedent in other domains.

3. On the Limits of the Contribution: The analysis of multiple outliers in Section 8.3 (page 12) suggests that the proposed method, which targets a single BM element, captures the lion's share of the benefit, with diminishing returns for handling a second or third outlier. Does this imply that the novel mechanism is fundamentally a "one-shot" trick, and that the residual problem of multiple co-located outliers requires reverting to more traditional (and less novel) techniques like grouping or explicit mixed precision? This would help frame the true boundary and impact of this specific novel contribution.