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REVIEW 4 major objections 6 minor 36 references

BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM Inference

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Each 32-value block picks its own 4-bit format from a 16-entry formatbook, and BlockDialect stays within 5.45% of full precision on LLaMA3-8B even with every matrix multiplication quantized.

desk verdict BlockDialect is a solid, well-engineered mixed-format quantization paper whose headline numbers are probably right but that reports no variance, tunes its block sizes on the same benchmarks it reports, and only tests prefill-style evaluation. read the letter →

arxiv 2501.01144 v5 pith:5ATZIG23 submitted 2025-01-02 cs.CL cs.LG

Wonsuk Jang , Thierry Tambe This is my paper · ORCID
classification cs.CLcs.LG
keywords BlockDialectFP4mixedformatquantization4-bitweight-activationpost-trainingfull-pathLLMinferenceMXFP4comparisonenergy-efficient
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

BlockDialect is a post-training quantization scheme built on a different question from most of the field: not 'how should each block be scaled down to 4 bits?' but 'which 4-bit number format should each block use?' The paper assembles a formatbook of 16 FP4 variants, called DialectFP4, that differ in their largest representable magnitudes, assigns each 32-value block the variant that best fits its data, and reports that this recovers nearly all the accuracy lost by the uniform MXFP4 format at the same or lower bit cost. On LLaMA3-8B and LLaMA2-7B, the method lands 5.45% and 2.69% below full precision even when every matrix multiplication, including the attention-path ones, is quantized. The reason to care is energy: all representable values are multiples of $0.5$, so the data path runs on 4-bit integer multiply-accumulate hardware whose synthesized area and power match plain FP4 units.

What carries the argument

The load-bearing object is DialectFP4, a formatbook of 16 FP4 variants built to match three profiled facts about blocks in LLMs: block maxima spread fairly evenly across the representable range, many blocks deviate from the matrix-wide distribution near their upper edge, and power-of-two shared exponents waste or underestimate range. Each dialect is a set of eight magnitude values with $0.5$ granularity; it keeps the six smallest values of base FP4 E2M1 and varies only its two largest values, so 16 dialects cover different dynamic ranges and different large-magnitude shapes while sharing most of their code space. The companion mechanism is a two-stage online selector: stage one truncates the block maximum to pick the pair of dialects sharing that maximum, and stage two counts, using AND/OR logic on 5-bit truncations, how many block elements fall into each dialect's 'beneficial range' — the interval where the differing large value actually reduces quantization error. Because every representable magnitude is a multiple of $0.5$, quantized operands become 4-bit unsigned integers before multiplication, and the MAC unit, shifter, and accumulator stay in integer arithmetic until partial sums are promoted to FP16.

What would settle it

Build a mirrored 16-dialect formatbook that instead prioritizes the smallest magnitudes (same $0.5$ granularity, same sharing structure) and rerun the full-path LLaMA3-8B experiment: if the small-priority book matches or beats the large-priority one, the importance premise fails. A finer probe is to instrument the selector and count, per block, how often the two-stage rule picks the dialect with the larger true MSE; a disagreement rate near chance on blocks whose maximum is an isolated outlier would show the heuristic is not tracking what the paper says it tracks.

Watch

Extended reading notes

Core claim

Uniform 4-bit formats lose accuracy because one shared format cannot represent the idiosyncratic spread of values inside each small block of an LLM tensor. The paper's central claim is that the residual error is mostly a representation problem rather than a scaling problem: if each block may choose among 16 FP4 'dialects' that differ only in how they cover the larger magnitudes, block-level distributions that standard FP4 handles poorly become representable. Concretely, BlockDialect with DialectFP4 beats MXFP4 by 10.78% average zero-shot accuracy on LLaMA3-8B (7.48% on LLaMA2-7B) at a lower effective bitwidth, stays within 1.76% and 1.20% of full precision when only linear layers are quantized, and within 5.45% and 2.69% for full-path quantization that includes attention activation-activation multiplies. The paper further claims a practical online two-stage dialect-selection heuristic that matches exhaustive MSE-based selection in accuracy while costing a tiny fraction of its hardware, and integer-only MACs whose area and power are essentially those of FP4 units.

Load-bearing premise

The whole design rests on the assumption that a block's larger-magnitude values are more important than its smaller ones, so the formatbook spends its scarce 4-bit entries on the large end and both selection stages reward capturing the largest values accurately; if small-magnitude elements mattered equally, the selector could pick the wrong dialect and BlockDialect's gains over MXFP4 would shrink.

Editorial extensions

If this is right

  • Full-path 4-bit weight-and-activation inference becomes accurate enough on 7-8B models that high-precision queries and attention-score paths can be dropped, with average zero-shot accuracy losses of 2.69-5.45%.
  • Effective bitwidth stays at or near MXFP4's (BlockDialect-32 uses 4.28 bits versus MXFP4-16's 4.31) while average zero-shot accuracy rises by 10.78 points on LLaMA3-8B, so a 4-bit dialect tag per 32-value block is cheap for the accuracy it buys.
  • Online activation format selection is implementable at inference speed: the two-stage logic meets timing at 250 MHz in 130 nm synthesis, while the MSE-based alternative fails at 100 MHz and uses roughly 10x the power and area.
  • The gains reproduce across LLaMA2-7B, LLaMA3-8B, Mistral-7B, OPT-6.7B, and smaller models, so the representational mismatch the formatbook targets looks like a general property of LLM blocks rather than a single-model artifact.
  • Combining BlockDialect with SmoothQuant-style scaling migration adds a small but consistent accuracy improvement, indicating representation choice and scaling choice are at least partially independent axes that can be tuned together.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural untested combination is BlockDialect with rotation-based outlier suppression, the Hadamard-transform family: the paper tests only SmoothQuant and notes the two axes are not fully orthogonal, but rotations attack the same outlier problem from a complementary direction that could compound with per-block format choice.
  • Because 15 of 16 dialects share six of eight values, the dialect tag carries almost no dequantization cost; carrying tags in registers for streaming blocks could push the realized per-data overhead below the paper's 9-bit-per-block accounting.
  • The chosen-dialect histogram per layer could double as a free distribution-drift monitor: if deployment-time inputs shift dialect selections away from calibration-time profiles, that is a signal the hardware already produces without extra instrumentation.
  • Extending the formatbook idea to other precisions (6-bit dialects, or per-block choice spanning FP4 and INT4) is the obvious next axis, and the paper's block-profiling procedure already gives the recipe for deciding which variants to include.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes BlockDialect, a block-wise mixed-format quantization technique for 4-bit weight-and-activation LLM inference. Instead of applying a single number format per tensor, BlockDialect selects, for each fine-grained block, one dialect from a fixed formatbook of FP4-like representations (DialectFP4, 16 dialects by default). A two-stage online selection mechanism picks the dialect for activation blocks without full MSE computation, and representative values are chosen with 0.5 granularity so that MAC operations reduce to 4-bit unsigned integer arithmetic. Experiments compare against MXFP4, LLM-FP4, and QuaRot on LLaMA-2-7B, LLaMA-3-8B, Mistral-7B, OPT-6.7B, and several smaller models, reporting WikiText2 perplexity and seven-task zero-shot accuracy under two scopes (linear-only and full-path including attention). Hardware synthesis at 45nm and 130nm is used to claim MAC units comparable in area and power to FP4, with small overhead for on-the-fly quantization. The central claim is that full-path 4-bit quantization is only 5.45% (LLaMA3) and 2.69% (LLaMA2) below full precision while outperforming MXFP4 by 10.78% and 7.48%, respectively, with lower effective bit usage.

Significance. If the empirical claims hold, the work is a substantive contribution: it reframes 4-bit quantization as a representation-selection problem rather than a scaling-only problem, and it provides a hardware-friendly integer path for mixed-format activation quantization. The paper has real strengths: wide model coverage (three 7-8B models plus OPT-6.7B, Phi-2.7B, LLaMA3-1B, GPT2-1.5B, MobileLLM-125M), full-path attention quantization in addition to linear layers, a two-stage selection method that is shown in Table 2 to closely match exact MSE selection, and synthesis-based hardware cost estimates. These are useful and falsifiable results. The main weaknesses are empirical verification issues: no seeds or error bars, configuration choices made after inspecting ablations on the same evaluation suite, a private code repository without a commit hash, prefill-only evaluation despite the decode-phase being the practically important regime, and a comparison framing that is favorable to the headline bit-usage claim. These issues do not invalidate the approach, but they do mean the headline numbers are not yet independently verifiable.

major comments (4)
  1. [Section 4.2, Tables 1 and 3] The headline all-scope numbers in Table 1 (LLaMA3: 69.00%, 5.45% below FP16; LLaMA2: 68.25%, 2.69% below FP16) are produced by the configuration “64 (dn,Q,K:16)”, which Section 4.2 selects after examining the block-size ablation in Table 3 on the same models and the same seven-task suite. No seeds, error bars, or per-task standard errors are reported, so the 10.78% and 5.45% margins are unbracketed and may reflect selection on the evaluation set. Please report multiple seeds or at least standard errors for the headline numbers, and describe a protocol that separates configuration selection from evaluation.
  2. [Section 4.1, Appendix C] The implementation footnote points to a private Stanford GitLab repository with no commit hash, so the central empirical claim cannot currently be independently re-run. Moreover, all Table 1 evaluations are prefill-style log-likelihood scores from lm-eval-harness; Appendix C describes a residual-FP16 KV-cache tail and per-token quantization for the decode phase, but the actual decode-phase configuration is never evaluated. The “full-path” claim should either be explicitly scoped to the prefill phase in the abstract and conclusion, or supplemented by decode-phase results (e.g., generative perplexity, throughput, or energy).
  3. [Abstract and Table 1] The “lower bit usage per data” claim in the abstract is baseline-dependent. In the all-scope rows of Table 1, BlockDialect-64 (dn,Q,K:16) uses W:4.25/A:4.21 on LLaMA3, which is lower than MXFP4-16 (4.31 bits) but higher than MXFP4-32 (4.16 bits). Against MXFP4-32 the LLaMA3 accuracy gain is 10.16% rather than 10.78%, and for LLaMA2 the gain is 8.49% with higher bit usage. Please report comparisons against both MXFP4-16 and MXFP4-32 with the exact effective bitwidths for the reported configurations, and qualify the “lower bit usage” statement accordingly.
  4. [Section 3.1 and Section 4.1] The DialectFP4 formatbook, including the number of dialects and the specific large-magnitude values, is designed from block-level profiling on WikiText2 (Section 3.1, Figures 2 and 7), and Table 1 then reports WikiText2 perplexity as an evaluation metric. For the perplexity numbers this is circular. Please state this limitation explicitly, and either design the formatbook on held-out calibration data or show that the formatbook and the dialect count are not sensitive to the calibration text.
minor comments (6)
  1. [Table 1] The column header “A VG.” appears to be a typo for “Avg.”; please correct it in Tables 1, 12, 13, and 14.
  2. [Section 4.1] The LLM-FP4 baseline description reports search interval (60,2) without stating whether this setting matches the original paper's defaults or how the baselines' own variance was handled; please specify the configuration and report standard deviations for the baselines as well.
  3. [Appendix H] The SmoothQuant alpha is selected as the value with the lowest perplexity on the same evaluation data, which is a form of calibration on the test metric; this should be acknowledged, and the comparison in Table 11 should state how much of the apparent gain comes from this selection.
  4. [Appendix C] The sentence “Note that BlockDialect’s full-path low-precision matrix multiplication is significantly more efficient during the prefill phase” qualifies the main full-path claim and should appear in the main text near the headline results, rather than only in the appendix.
  5. [Figure 4 and Section 3.3] Figure 4 lists only magnitude values; since a sign bit is stored separately, the figure caption should state explicitly that the 4-bit index encodes magnitude only and that the sign bit is handled at the MAC level.
  6. [Appendix I] The effective-bitwidth calculation assumes a sequence length of 2048; because attention operand bit costs depend on sequence length, please state how the bit-count comparison changes at shorter or longer sequence lengths.

Circularity Check

3 steps flagged · score 5.0 of 10

The 16-dialect formatbook is derived from the same models and the same WikiText2 activation distribution it later 'validates' with, and the headline block sizes are tuned on the same benchmark suite, so part of the reported superiority is self-referential; the central claim nonetheless has external content (held-out zero-shot tasks, MSE comparison, hardware synthesis).

  1. other [Section 3.1 'Which Dialects Should be Used?' and Section 4.1 'Models and dataset' (perplexity on WikiText2); abstract and Table 1.]
    "To provide a guideline for determining dialects for the formatbook, we conduct profiling of Llama3-8B ... using WikiText2 ... [Figure 2] ... Based on this, we select FP4 E2M1 ... as the base format for our dialects. ... 16-Dialect DialectFP4 Example. Figure 4 illustrates 16-dialect formatbook, DialectFP4, that meets our three key requirements ... Additionally, we report perplexity scores on WikiText2 (Merity et al., 2016) with a chunk of 2048."

    The dialect list is constructed to match the empirical magnitude histogram of the target models computed on WikiText2 (e.g., the evenly distributed block maxima in Figure 2b motivate the 16 dialects). The same dataset distribution is then reported as a downstream 'result': Table 1's WikiText2 perplexity (7.05, 7.87, etc.). Because the formatbook is fit to this very distribution and the same models, the low WikiText2 perplexity is partly a re-measurement of the fitting target rather than an independent validation. The zero-shot tasks are held out and the per-task numbers are external, so the circularity is only partial.

  2. other [Section 4.2 'Impact of Block Size' (Table 3) and Section 4.1 'Models and dataset' (seven zero-shot common-sense reasoning tasks) cross-referenced with Table 1.]
    "We further investigate dynamic block size assignment by applying small blocks to specific projection layers to assess block size sensitivity across sublayers. As in Table 3, down projection has higher sensitivity ... Based on these findings, we obtain comparable or superior results with block size of 64 by applying smaller blocks only to sublayers prone to outliers ... compared to a uniform block size of 32 in the all scope."

    The headline claim (10.78% / 7.48% over MXFP4 and only 5.45% / 2.69% below FP16) is reported only for the configuration selected after inspecting Table 3, which is itself computed on the same models and same seven-task benchmark as the headline. The '64 with dn:16 and Q,K:16' choice is thus a benchmark-optimized configuration, not an a priori design choice; presenting its numbers as the unconditional result of BlockDialect overstates the out-of-sample content. This is a test-set-tuned-configuration pattern, not a fitted-parameter-renamed-as-prediction pattern: the gains are real reported measurements but the specific margins are configuration-selected.

1 more flagged steps
  1. self citation load bearing [Section 3.3 'How Should Online Quantization and MAC Operations be Performed?' and Section 4.3 'Hardware Cost Analysis' (Table 7); footnote 4, https://code.stanford.edu/tambe-lab/blockdialect.]
    "For performance evaluation, we implement the BlockDialect emulation framework 4 on top of HuggingFace Transformers using PyTorch. ... For hardware comparison, we model multiply-accumulate (MAC) units ... using SystemVerilog and synthesize them with Synopsys Design Compiler. ... MSE-based logic is 9.32x larger and consumes 9.86x more power."

    The two-stage selection is claimed to be validated by the hardware numbers in Table 7, and the emulation framework is hosted at a private Stanford GitLab URL with no commit hash. The hardware synthesis is a code artifact whose correctness is asserted in this paper alone; it is machine-checkable in principle but not machine-checked in the manuscript, and no external benchmark reproduces the 9.32x/9.86x overhead or the 5-cycle latency. This is not circular in the sense of Eq. X = Eq. Y, but it is a load-bearing, non-independent self-report: the paper's own implementation and own synthesis are the only evidence for the 'practical' claim.

full rationale

BlockDialect is an empirical systems paper, and most of its central claim is externally grounded: the zero-shot accuracies in Table 1 and Appendix J are measured with lm-eval-harness on held-out tasks (LAMBADA, HellaSwag, BoolQ, PIQA, WinoGrande, ARC-easy, ARC-challenge) that were not used to build DialectFP4's formatbook; the comparison to MSE-based dialect selection in Table 2 is a genuine algorithmic comparison; and the MAC synthesis numbers (Tables 5-7) are concrete engineering estimates. So a score of 0 is inappropriate. However, several load-bearing elements do reduce to their own inputs. (1) The very design of DialectFP4 was fit to WikiText2 histograms of the exact models under test, and WikiText2 perplexity is then reported as a validation result, making the WikiText2 component self-referential; a reviewer's score of 3 should be raised because this is not a minor citation issue but calibration of the method's central artifact on a reported metric. (2) The headline gains are quoted only for the configuration '64 (dn, Q, K:16)', chosen after inspecting the block-size ablation on the same benchmark and same models, with no held-out configuration selection, seeds, or error bars; the specific 10.78%/5.45% margins are therefore partly selected rather than predicted. (3) The hardware-efficiency claim rests on the authors' private emulation and synthesis code without independent reproduction. I did not count the weaker elements that the skeptic raised: the 'larger magnitudes matter' heuristic is explicitly tested against MSE selection in Table 2 rather than being assumed circular; and the MXFP4-32 comparison is reported in Table 1, so the 'lower bit usage' framing is a presentation choice, not a circular step. Overall score: 5 because one of the paper's two headline metrics (WikiText2 perplexity) partially validates its own fitting distribution and the headline configuration is benchmark-tuned, yet the zero-shot and hardware claims still have independent content.

Assumptions & free parameters 5 free parameters · 5 assumptions · 1 invented entities

The central method rests on a hand-designed codebook (the 16 dialect tables), a small set of hyperparameters (dialect count, block sizes, shared-exponent shift) selected on the evaluation benchmarks, and domain assumptions about magnitude importance and hardware support. The invented entity, DialectFP4, is the method's core artifact and is testable via the reported benchmarks. No physical entities or new mathematical structures are introduced.

free parameters (5)
  • DialectFP4 formatbook values (16 dialect tables) = 16 tables of 8 magnitudes each, e.g. dialect 0: [7.5,5.5,3,2,1.5,1,0.5,0]; granularity 0.5
    Hand-designed from WikiText2 profiling of target models (Section 3.1, Figure 4); the specific large-magnitude values and the 0.5 minimum granularity are author choices, not derived from first principles.
  • Number of dialects = 16
    Selected via ablation comparing 8, 16, and 24 dialects on the same evaluation benchmark (Table 4).
  • Block size configuration = 32 default; 64 with exceptions dn,Q,K at 16
    Chosen based on sensitivity analysis on the same models and tasks (Table 3); smaller blocks improve accuracy for down projection and attention query and key at some bitwidth cost.
  • Shared exponent shift = -2 applied to floor(log2(max))
    Normalization so block magnitudes fall in [0,8), shaped to match FP4 E2M1's range [0,6] (Section 3.1); a design constant that determines where dialects sit.
  • SmoothQuant alpha (Appendix H only) = tuned per model, granularity 0.05
    Used only in the exploratory synergy study; not part of the central BlockDialect claim.
assumptions (5)
  • domain assumption Larger magnitudes in a block are more important for quantization accuracy.
    Stated in Section 3.1; underpins formatbook design and the two-stage selection's beneficial-range counting.
  • domain assumption The magnitude distribution profiled on WikiText2 for layers 0,10,20,30 of four models generalizes to other layers, models, and workloads.
    Section 3.1 and Appendix A; the formatbook is fixed from these profiles and then applied to other layers and models.
  • domain assumption Hardware supports per-block power-of-two shared exponents with affordable overhead (MX-style scaling), including a 4-bit dialect identifier per block.
    Section 2.1 and Section 4.3; the energy-efficiency claims depend on this hardware support.
  • domain assumption The beneficial-range midpoint rule approximates the MSE-optimal dialect choice for each block.
    Section 3.2; the counting heuristic uses midpoints between adjacent representable values as decision boundaries; no proof given, only an empirical comparison to MSE (Table 2).
  • standard math Truncation to the 5-bit intermediate representation (3 integer bits, 2 fractional bits) does not materially change dialect selection or quantization outcomes.
    Section 3.2 preprocessing truncates lower bits; the paper assumes this is benign.
invented entities (1)
  • DialectFP4 formatbook independent evidence
    purpose: A set of 16 FP4 variants used to represent each block with a tailored number format.
    The formatbook has falsifiable handles: benchmark accuracy results and synthesized hardware costs reported in this paper, though its values are hand-designed rather than derived.

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Cite this review

Pith. "Pith review of BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM Inference." pith.science (2026). https://pith.science/paper/5ATZIG23

@misc{pith2026250101144,
  author       = {Pith},
  title        = {Pith review of: BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM Inference},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5ATZIG23}},
  note         = {Machine review of arXiv:2501.01144}
}
read the original abstract

The rapidly increasing size of large language models (LLMs) presents significant challenges in memory usage and computational costs. Quantizing both weights and activations can address these issues, with hardware-supported fine-grained scaling emerging as a promising solution to mitigate outliers. However, existing methods struggle to capture nuanced block data distributions. We propose BlockDialect, a block-wise fine-grained mixed format technique that assigns a per-block optimal number format from a formatbook for better data representation. Additionally, we introduce DialectFP4, a formatbook of FP4 variants (akin to dialects) that adapt to diverse data distributions. To leverage this efficiently, we propose a two-stage approach for online DialectFP4 activation quantization. Importantly, DialectFP4 ensures energy efficiency by selecting representable values as scaled integers compatible with low-precision integer arithmetic. BlockDialect achieves 10.78% (7.48%) accuracy gain on the LLaMA3-8B (LLaMA2-7B) model compared to MXFP4 format with lower bit usage per data, while being only 5.45% (2.69%) below full precision even when quantizing full-path matrix multiplication. Focusing on how to represent over how to scale, our work presents a promising path for energy-efficient LLM inference.

Figures

Figures reproduced from arXiv: 2501.01144 by the authors.

Figure 1
Figure 1. Overview of BlockDialect technique workflow. lami et al., 2024) and increased computational costs (Xiao et al., 2023). By reducing numerical precision, quantiza￾tion effectively reduces memory usage and data movement overhead (Kim et al., 2023). Additionally, leveraging low￾precision operation results in improvements in inference speed, area, and energy efficiency (Xiao et al., 2023; Cao et al., 2024; Rouhani et al.… view at source ↗
Figure 2
Figure 2. LLaMA3-8B block-level profiling results: (a) matrix￾wise accumulated magnitude distribution, (b) block’s maximum magnitude distribution. Each bar represents the average across layers 0, 10, 20, and 30, with consistent trends across layers. of variants. Additionally, unlike prior methods that rely on calibration or pre-training to reduce online activation processing overhead, BlockDialect supports efficient online pr… view at source ↗
Figure 3
Figure 3. Core observations shaping the formatbook design: (a) wasted or underestimated ranges, (b) block-specific distributions deviating from the matrix-wise pattern. [0, 2), while FP4 E2M1 spans [0, 6]. Subtracting 2 from the shared exponent shifts the range to [0, 8), enabling di￾rect comparison with FP4 E2M1. Note that we leverage hardware-supported scaling with a power-of-two scaling factor, resulting in a power-of-two … view at source ↗
Figures from the paper (5 more)
Figure 5
Figure 5. Figure 5: Per-block dialect selection process: a) overall process, b) 1st stage, and c) 2nd stage. Dialect 4 6.5 5.0 Common 5 3.0 4 2.0 3 1.5 2 1.0 1 0.5 0 0 … Idx 7 6 … Optimal dialect index: 4’d4 Quantized 4-bit data: 4’b1 110 0.5 ∙ -10 = - 5.0 Dialect 5 6.5 4.0 Dialect 6 6.0 …
Figure 6
Figure 6. Figure 6: Overview of dequantization process. the optimal dialect. For example, consider preprocessed data (M agtrunc in [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: LLaMA2-7B (a,b), Mistral-7B (c,d), and OPT-6.7B (e,f) Block-level profiling results: (a), (c), (e) matrix-wise accumulated magnitude distribution, (b), (d), (f) block’s maximum magnitude distribution [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Selection ratio of each dialect for (a) LLaMA3-8B, (b) LLaMA2-7B, (c) Mistral-7B, and (d) OPT-6.7B. Experiments were conducted on Wikitext2 with a block size of 32. Each bar represents the average across layers 0, 10, 20, and 30. C. Quantization Dimension 4-bit Sequenc…
Figure 9
Figure 9. Figure 9: Proposed KV cache structure: (a) challenge of sub-channel-wise value quantization, (b) proposed cache structure. BlockDialect quantizes matrices and vectors along their respective multiplication dimensions. For example, in activation￾weight multiplication, activations …

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Reviewed August 10, 2026 · model on record in the stance chip above.