Background and why MoDeGPT matters for LLM compression
MoDeGPT is a structured compression framework for large language models that eliminates recovery fine-tuning from the compression pipeline entirely. Rather than applying weight decomposition matrix-by-matrix and then running gradient-based recovery to recapture lost accuracy, MoDeGPT partitions each Transformer block into modules composed of weight matrix pairs and reconstructs module-level outputs directly. The paper reports this approach saves 98% of compute costs when compressing a 13B-parameter model while preserving 90–95% of zero-shot task performance at 25–30% compression on Llama-2, Llama-3, and OPT.
The practical significance is in the removal of the recovery step. Most structured compression pipelines treating LLM compression seriously require at least LoRA-style recovery fine-tuning or a calibration-based gradient pass after the structural modification. MoDeGPT's claim is that joint module-level reconstruction serves as a sufficiently accurate local proxy for the true loss, making recovery unnecessary in the tested settings.
Bottom Line: MoDeGPT (arXiv:2408.09632) is a training-free structured compression method that jointly decomposes weight matrix pairs within Transformer modules — MLP, attention, and projection sublayers — using classical approximation methods (Nyström, CR decomposition, SVD). On Llama-2/3 and OPT at 25–30% compression, it reports 90–95% zero-shot retention, up to 46% inference throughput improvement, and 98% compute-cost savings versus gradient-based recovery pipelines for a 13B model. These results are empirical and model-family-specific; the method is not a universal pruning recipe and has not been benchmarked on sparse MoE architectures.
What problem MoDeGPT is trying to solve in Transformer compression
The central bottleneck MoDeGPT targets is not compression itself but the recovery phase that follows structured compression. Structured compression methods that physically remove dimensions from weight matrices — unlike quantization or unstructured sparsity, which leave the weight shape intact — must somehow repair the accuracy damage that follows. The dominant repair mechanism is recovery fine-tuning: a gradient-based pass that updates the remaining weights to compensate for the removed capacity.
For 13B-scale models, recovery fine-tuning is expensive. It requires backward propagation through the compressed network, access to a training-relevant data distribution, and sufficient GPU hours to converge. MoDeGPT's formulation side-steps this entirely: by reconstructing module-level outputs rather than matrix-level outputs, the method positions local reconstruction error as the objective, which it claims is a good enough proxy for downstream task performance that no recovery step is required.
As the abstract states: "This paper introduces Modular Decomposition (MoDeGPT), a novel structured compression framework that does not need recovery fine-tuning while resolving the above drawbacks."
Pro Tip: When evaluating structured compression papers, distinguish between the compression cost (the one-time forward-pass or decomposition compute) and the recovery cost (fine-tuning after compression). MoDeGPT's 98% compute savings claim is about the recovery elimination, not about making the decomposition itself faster. For practitioners doing repeated compression experiments, removing recovery is the larger operational unlock.
Why gradient-driven compression pipelines are expensive to recover
Gradient-driven recovery after structural compression is expensive because it is functionally equivalent to partial retraining. After dimensions are removed, the compressed model has a different representational capacity; the remaining weights must be updated jointly across layers to redistribute the lost expressiveness. This requires maintaining activations and gradients across the full network depth, which on a 13B model at bf16 precision occupies tens of gigabytes of activation memory per forward-backward pass — even with gradient checkpointing.
The MoDeGPT paper directly addresses this cost: "Our comprehensive experiments show MoDeGPT, without backward propagation, matches or surpasses previous structured compression methods that rely on gradient information, and saves 98% of compute costs on compressing a 13B model." The 98% figure is reported relative to gradient-based structured compression competitors in the paper's experimental setup; it is not a statement about every possible 13B compression pipeline or every hardware configuration.
Watch Out: The 98% compute-cost savings figure applies specifically to MoDeGPT's compression run on a 13B model versus gradient-based recovery competitors in the paper. The paper does not provide wall-clock equivalents across GPU types, and the figure should not be extrapolated to other model sizes, compression ratios, or compared against quantization-based methods that never require backward propagation regardless.
Where MoDeGPT fits relative to pruning, quantization, and low-rank methods
MoDeGPT occupies a specific niche in the LLM compression taxonomy: fully-structured, training-free, module-level low-rank decomposition. It is not quantization (weight values remain in float; matrix shapes change), not unstructured sparsity (no individual weight zeroing), and not LoRA-style adaptation (no added adapter parameters). Its closest structural relatives are SliceGPT and SparseGPT, both of which also target structured compression, but which rely on gradient or Hessian information for their respective weight updates. Structured pruning is the nearest umbrella term for the class of methods that physically remove model structure rather than only reducing numeric precision.
The OpenReview record for MoDeGPT contrasts the method explicitly with coarser structured approaches: "MoDeGPT leverages fully-structured compression, resulting in decent throughput speedup without the need for specialized GPU support. In contrast, methods like ShortGPT and SLEB rely on coarse compression strategies (e.g., layer pruning), achieving faster speedups but at the cost of accuracy loss." This positions MoDeGPT in the middle of the accuracy-speed trade-off curve among structured methods.
| Method | Compression type | Requires backward pass | Throughput gain | Accuracy trade-off |
|---|---|---|---|---|
| MoDeGPT | Structured low-rank (module-level) | No | Up to 46% (reported) | 5–10% zero-shot loss at 25–30% compression |
| SliceGPT | Structured (slice removal) | Yes (calibration) | Moderate | Comparable to MoDeGPT range |
| SparseGPT | Unstructured/semi-structured sparsity | No (second-order approx.) | Hardware-dependent | Task-specific |
| Layer pruning (ShortGPT/SLEB) | Coarse structured | No | Higher | Higher accuracy cost |
| Post-training quantization | Quantization | No | Hardware-dependent | Typically <1% at 4-bit |
Quantization achieves better accuracy retention at lower compute cost for standard inference serving scenarios; MoDeGPT is the more relevant method when you need physically reduced matrix dimensions — for memory-bandwidth-bound inference or when downstream kernel support for a given quantization format is unavailable.
How modular decomposition works inside Transformer blocks
MoDeGPT's core mechanism is output-level reconstruction at module granularity rather than at individual matrix granularity. A Transformer block contains multiple weight matrices — Q, K, V, output projection in attention; up-projection, gate, down-projection in MLP — and naive SVD-based compression treats each matrix independently, minimizing per-matrix reconstruction error. MoDeGPT instead groups these matrices into modules (matrix pairs or triplets) and minimizes the reconstruction error of the module's output activation, not each constituent matrix. That block partitioning is what allows the method to avoid a recovery fine-tuning stage: the calibration pass is aimed at reproducing the module output directly, so the compressed block is tuned to match the forward computation the model actually uses.
flowchart LR
subgraph TransformerBlock["Transformer Block"]
direction TB
subgraph ATT["Attention Module"]
QKV["Q, K, V projections\n(matrix pair)"]
OUT["Output projection"]
QKV --> OUT
end
subgraph MLP["MLP Module"]
UP["Up / Gate projections\n(matrix pair)"]
DOWN["Down projection"]
UP --> DOWN
end
end
CAL["Calibration data\n(forward pass only)"] --> ATT
CAL --> MLP
ATT -->|"Nyström / CR\ndecomposition"| CATT["Compressed\nAttention Module"]
MLP -->|"SVD / CR\ndecomposition"| CMLP["Compressed\nMLP Module"]
CATT --> OUT2["Reconstructed\nmodule output"]
CMLP --> OUT2
The key insight is that minimizing the error at the module output is a tighter proxy for downstream loss than minimizing per-matrix Frobenius error. When you compress Q, K, and V jointly — enforcing that the combined attention computation reproduces the original module output on calibration data — the decomposition accounts for inter-matrix dependencies that independent compression ignores. No backward propagation through the loss function is required; the calibration data drives a forward pass, and the decomposition is solved analytically.
Module grouping for MLP, attention, and projection sublayers
MoDeGPT groups Transformer sublayers into modules defined by functional output boundaries. The abstract describes this as partitioning "the Transformer block into modules comprised of matrix pairs," with hidden dimensions reduced by reconstructing module-level outputs. In practice this means the attention sublayer's Q/K/V projection matrices and the output projection matrix form one module, while the MLP sublayer's up-projection (and gate in gated architectures like Llama) paired with the down-projection form another. Each module is treated as a coupled system for the purposes of the decomposition.
This grouping reflects a structural property of Transformer computation: the value of compressing Q and K jointly is that their interaction through the attention score matrix means errors in one can partially compensate for errors in the other if the decomposition is solved at the module level. Similarly, in a gated MLP, the gate and up projections interact element-wise before the down projection, so their errors are non-independent.
Pro Tip: The module boundary choice in MoDeGPT is architecturally motivated, not arbitrary. If you adapt the method to a non-standard Transformer variant — one with, say, grouped-query attention with asymmetric head counts, or a hybrid attention-MLP variant — you must re-examine whether the default module groupings still correspond to the correct functional output boundaries in your specific architecture. The paper's groupings are validated on standard Llama and OPT block layouts.
Why joint decomposition beats treating matrices independently
Independent matrix compression — the default SVD approach applied naively — minimizes each matrix's own Frobenius reconstruction error without regard for how that matrix's output feeds into the next operation. At moderate compression ratios this is sufficient, but at 25–30% compression the accumulated cross-matrix error compounds layer by layer and produces perplexity degradation that cannot be recovered without fine-tuning.
Joint module decomposition reframes the objective: instead of minimizing (|W - \hat{W}|_F) for each matrix $W$, MoDeGPT minimizes the error on the module's output activations given calibration data. This is the empirical basis for the paper's claim that "using local reconstruction as a proxy for true loss can achieve state-of-the-art compression." The local reconstruction objective captures the compound effect of multiple matrices interacting, which independent matrix compression cannot model.
The practical consequence: at 25–30% compression on Llama-2 and Llama-3, MoDeGPT's joint decomposition produces lower zero-shot degradation than prior structured methods that required gradient recovery to reach comparable accuracy. The paper reports this as matching or surpassing gradient-based methods — meaning the output-reconstruction objective is doing the work that fine-tuning previously did.
Watch Out: The empirical superiority of joint decomposition over independent matrix compression is demonstrated within the paper's tested settings (Llama-2/3, OPT, 25–30% compression). This is not a universal theorem. At higher compression ratios — above 30% — the local reconstruction proxy becomes a less reliable surrogate for task loss, and the advantage over independent matrix compression or gradient-based recovery methods may not hold. The paper's results should not be read as implying joint decomposition always dominates.
How Nyström approximation, CR decomposition, and SVD are assigned to module types
MoDeGPT maps different module types to three classical matrix approximation methods based on their structural properties. The assignment is not arbitrary: each method exploits a specific algebraic structure in the weight matrices or their interaction.
SVD handles modules where the weight matrix has a compact singular value spectrum — effective rank is significantly lower than the nominal rank. In these cases, truncated SVD produces a near-optimal low-rank factorization with controllable approximation error.
Nyström approximation applies to square or symmetric Gram matrix settings, typically arising when the module output involves an inner product structure (as in attention score computation). The Nyström method approximates a positive semi-definite matrix using a selected column subset, which makes it computationally cheaper than full SVD for large symmetric matrices.
CR decomposition (column-row decomposition) selects actual columns and rows from the original matrix to form the approximation, preserving sparsity structure and interpretability. This is assigned to modules where preserving exact column or row vectors from the original weight matrix is important for numerical stability.
The general form of a rank-$r$ approximation across all three methods can be written as:
$(\hat{W} = C R)$
where (C \in \mathbb{R}^{m \times r}) and (R \in \mathbb{R}^{r \times n}) are the low-rank factors, with the specific construction of $C$ and $R$ differing by method:
- SVD: (C = U_r \Sigma_r^{1/2}), (R = \Sigma_r^{1/2} V_r^\top)
- Nyström: $C$ consists of selected columns of the Gram matrix; $R$ is the pseudo-inverse bridge
- CR decomposition: $C$ contains $r$ selected columns of $W$; $R$ contains $r$ selected rows of $W$ with an appropriate correction term
The exact per-module assignment of these three methods in MoDeGPT's formulation is detailed in the full method section of arXiv:2408.09632v1. Readers implementing or extending this method should consult that section directly rather than inferring from the abstract alone.
Empirical results reported for Llama-2, Llama-3, and OPT
The paper's central empirical claim is that MoDeGPT achieves 90–95% zero-shot task performance retention at 25–30% structured compression on Llama-2/3 and OPT, while requiring no backward propagation and completing the compression on a single GPU within a few hours. It also reports inference throughput increases of up to 46% and 98% compute-cost savings versus gradient-based recovery competitors on a 13B model compression run.
| Metric | Value | Condition |
|---|---|---|
| Zero-shot performance retention | 90–95% | 25–30% compression, Llama-2/3, OPT |
| Inference throughput gain | Up to 46% | Best-case across reported settings |
| Compression compute-cost savings | 98% | vs. gradient-based methods, 13B model |
| Compression hardware requirement | Single GPU | A few hours |
| Methods surpassed | SliceGPT, SparseGPT (structured) | Per paper's reported benchmarks |
The comparison to SliceGPT and SparseGPT is framed in terms of the paper's reported structured compression benchmarks. MoDeGPT claims to match or surpass these methods while eliminating the recovery fine-tuning that SliceGPT's pipeline requires and operating without second-order gradient information that SparseGPT uses. The throughput gains come directly from physically reduced matrix dimensions, which decrease memory bandwidth requirements and FLOPs per forward pass — both of which translate to measurable throughput improvement on standard GPU hardware without specialized sparse kernels.
Zero-shot retention at 25% to 30% compression
The 90–95% zero-shot retention figure reported in the paper covers the 25–30% compression range on Llama-2, Llama-3, and OPT. As the abstract states: "On LLaMA-2/3 and OPT models, MoDeGPT retains 90-95% of zero-shot performance with compression rates of 25-30%."
| Model family | Compression rate | Zero-shot retention |
|---|---|---|
| Llama-2 | 25–30% | 90–95% (reported range) |
| Llama-3 | 25–30% | 90–95% (reported range) |
| OPT | 25–30% | 90–95% (reported range) |
Two constraints bound interpretation of these numbers. First, the paper reports a performance retention range across the 25–30% compression window, not a monotonically stable plateau; the retention at 30% may be closer to 90% while 25% compression retains closer to 95%. Second, the abstract excerpt does not specify which zero-shot benchmark tasks constitute the evaluation suite — standard practice in LLM compression papers is to use common-sense reasoning tasks (HellaSwag, WinoGrande, PIQA, ARC), but practitioners should verify the specific task composition in the full paper before drawing task-specific conclusions.
Throughput gain and compute-cost savings on a 13B model
The throughput gain of up to 46% and the 98% compression-compute savings are the two headline efficiency claims. They measure different things and should not be conflated.
| Efficiency claim | What it measures | Reported value | Baseline |
|---|---|---|---|
| Throughput gain | Inference tokens/sec on compressed model | Up to 46% | Uncompressed baseline |
| Compute-cost savings | Cost of the compression procedure itself | 98% | Gradient-based recovery competitors |
| Compression time | Wall-clock for the compression run | A few hours | Single GPU |
The 46% throughput gain is an upper-bound figure ("up to") derived from the physically smaller matrix dimensions post-compression, which reduce FLOPs and memory reads per forward pass. The realized gain in a production serving environment will depend on batch size, sequence length, and whether the serving runtime takes advantage of the reduced dimensions (e.g., through kernel-level optimizations for the new matrix shapes). The 98% compute savings is entirely about the compression pipeline itself: by avoiding backward propagation and recovery fine-tuning, MoDeGPT's compression run costs a fraction of what a gradient-based approach would require for a 13B model.
What the paper's results do not justify
The MoDeGPT paper establishes that joint module-level decomposition without recovery fine-tuning is viable for Llama-2/3 and OPT at 25–30% compression. It does not establish universality. Several extrapolations are technically unsupported by the reported evidence.
The OpenReview record itself notes that MoDeGPT's fully-structured compression trades some peak speedup against accuracy: coarser methods like ShortGPT achieve faster throughput improvements but at higher accuracy cost. The paper's claim is not that MoDeGPT always achieves the best accuracy-speed trade-off — it is that it achieves a better trade-off than prior structured methods at the specific compression ratios tested.
Watch Out: MoDeGPT's reported results do not justify the following extrapolations: (1) that the method works without accuracy regression at compression rates above 30%; (2) that the 90–95% retention holds on architectures beyond Llama-2/3 and OPT; (3) that the 46% throughput gain transfers to every batch size, sequence length, or hardware setup; (4) that training-free compression is production-safe without task-specific evaluation. Each of these is a separate empirical question the paper does not answer.
When reported gains may not transfer to other architectures
The model families tested — Llama-2, Llama-3, and OPT — share a common decoder-only Transformer structure with standard MLP and multi-head or grouped-query attention blocks. The module groupings MoDeGPT defines are calibrated for this topology. Architectures that diverge from this standard — models with hybrid attention mechanisms, different normalization placement, sliding-window attention (as in Mistral), or mixture-of-expert routing layers — may not map cleanly to MoDeGPT's module partition scheme.
The paper's abstract explicitly scopes its empirical claims: "On LLaMA-2/3 and OPT models..." — it does not claim cross-family robustness. For practitioners targeting models outside this family (Falcon, Mistral, Phi-3, Gemma-2, or any MoE model), MoDeGPT should be treated as a strong starting point requiring architecture-specific validation, not a drop-in compression recipe.
Pro Tip: Before applying MoDeGPT to a non-Llama/OPT architecture, audit the module boundary assumptions. Specifically: verify that your architecture's attention and MLP sublayer boundaries correspond to the matrix-pair groupings the method expects. GQA variants with asymmetric Q/KV dimensions, or architectures with fused QKV projections, may require re-derivation of the module groupings to correctly minimize output reconstruction error.
Why training-free does not mean zero-risk for production use
Training-free compression eliminates the gradient-based recovery cost, but it does not eliminate the need for post-compression validation before production deployment. MoDeGPT's compression is calibration-data-driven: the module output reconstruction objective is minimized on a fixed calibration set, and the quality of the compressed model depends on how representative that calibration set is of the actual inference distribution.
Production Note: Deploying a MoDeGPT-compressed model without task-specific evaluation introduces several risks that zero-shot retention percentages do not capture. Calibration distribution mismatch can cause selective degradation on out-of-distribution queries. Physically reduced matrix dimensions change kernel dispatch behavior in serving frameworks (vLLM, TensorRT-LLM), and the throughput gain assumes the runtime correctly handles the new weight shapes without fallback to unoptimized code paths. Always run: (1) regression evaluation on your production query distribution, (2) latency profiling under your target batch size and sequence length, and (3) A/B comparison against quantization as an alternative compression path before committing to structured decomposition in production.
What MoDeGPT implies for MoE-adjacent model families
MoDeGPT was developed and validated on dense decoder Transformers. Its applicability to MoE-adjacent models like Mixtral 8×7B or DeepSeek-V3 is structurally plausible but empirically unverified. The method's strength — physically reduced matrix dimensions without specialized hardware support — is directly relevant to MoE serving contexts, but the provided facts do not include any verified MoE-family benchmark numbers, so performance retention on Mixtral or DeepSeek-V3 should not be implied as measured evidence.
The OpenReview record establishes that MoDeGPT's structured LLM compression produces throughput improvement without requiring specialized GPU support, which is a relevant property for MoE inference: MoE serving stacks typically cannot rely on custom sparsity kernels across all hardware targets, so a compression method that delivers gains through standard dense matrix operations on smaller dimensions is architecturally compatible with existing MoE serving infrastructure.
| Property | Dense Transformer (Llama-3) | MoE (Mixtral/DeepSeek-V3) | MoDeGPT compatibility |
|---|---|---|---|
| Standard MLP/attention blocks | Yes | Shared layers only | Directly applicable |
| Expert FFN blocks | N/A | Per-expert | Requires per-expert grouping validation |
| Routing mechanism | N/A | Top-K gating | Not compressed by MoDeGPT |
| Throughput bottleneck | Compute-bound (large batch) | Variable by serving setup | Reduction in matrix size remains relevant |
| Recovery fine-tuning cost | High | Higher (expert interactions) | MoDeGPT's training-free property more valuable |
Memory bandwidth, expert imbalance, and debugging constraints in sparse MoE stacks
Dense-model engineers moving to MoE serving should treat the stack as a separate validation problem rather than assuming that dense-model compression behavior transfers cleanly. The main risks are architectural mismatch, expert grouping, and router interaction with compressed experts; those are the issues that require validation, not the unverified operational claims sometimes attached to MoE systems.
Second, expert imbalance: production MoE deployments with non-uniform input distributions frequently exhibit load imbalance across experts.
Third, debugging: structured compression changes weight shapes permanently. When a compressed MoE model produces degraded outputs on a specific query type, diagnosing whether the degradation originates in the routing logic, in the shared attention layers, or in a specific expert's compressed MLP requires tooling that can trace activation paths at expert granularity.
Pro Tip: For MoE-targeted structured compression, validate the router output distribution and compare it against the expected production activation mix before treating the compressed experts as representative of the target workload. This is a recommendation, not a benchmark result, and it should be paired with task-specific evaluation rather than assumed to generalize.
Which benchmark facts are relevant for practitioners and which are paper-only
The distinction between reproduction-relevant compute claims and deployment runtime specs is important when reading MoDeGPT's results. The practically actionable numbers from the paper are:
| Claim | Practitioner relevance | Caveat |
|---|---|---|
| Single GPU, few hours for 13B compression | Directly useful for resource planning | GPU type and calibration set size not specified |
| 98% compute savings vs. gradient recovery | Relevant for comparing against recovery-based pipelines | Relative to paper's baseline methods only |
| 90–95% zero-shot retention at 25–30% compression | Useful as a floor estimate for planning | Llama-2/3 and OPT only; task suite unspecified |
| Up to 46% throughput gain | Upper-bound planning figure | Serving framework and batch config determine realized gain |
What the paper does not provide: memory footprint guarantees post-compression, latency percentile data (p50/p99), serving-framework-specific throughput benchmarks, or deployment runtime SLAs.
Watch Out: The "single GPU within a few hours" statement is a compression-procedure claim, not a deployment guarantee. The paper does not specify the GPU model, VRAM capacity, calibration dataset size, or whether any host-to-device data movement is included in that estimate. Before quoting this number in a capacity plan, verify it against the full paper's experimental setup section to understand the exact hardware configuration.
FAQ on MoDeGPT, recovery fine-tuning, and benchmark interpretation
What is MoDeGPT? MoDeGPT is a structured compression framework for large language models that compresses Transformer blocks by jointly decomposing weight matrix pairs within functional modules, minimizing module-level output reconstruction error. It maps module types to Nyström approximation, CR decomposition, or SVD depending on their structural properties. Described in arXiv:2408.09632, it was accepted at ICLR 2025 (OpenReview ID: 8EfxjTCg2k).
Does MoDeGPT work without backward propagation? Yes, within the scope of its reported experiments. The compression procedure uses only forward passes over calibration data to compute the module output reconstruction objective. No gradients are propagated back through the network, and no recovery fine-tuning pass is performed. This distinguishes it from SliceGPT and similar structured methods that require either calibration-time gradient computation or post-compression fine-tuning.
How does MoDeGPT compare with SVD and SliceGPT? Naive SVD compresses matrices independently, minimizing per-matrix Frobenius error without accounting for inter-matrix interactions within a module. MoDeGPT uses SVD as one of three decomposition methods but applies it within a joint module reconstruction framework that produces lower downstream accuracy loss than independent per-matrix SVD. SliceGPT applies structured slicing based on calibration data but involves gradient information for weight updates. MoDeGPT achieves comparable or better accuracy at 25–30% compression on Llama-2/3 and OPT without the gradient pass, according to its reported benchmarks.
What are the strengths of MoDeGPT? Three are empirically supported: elimination of recovery fine-tuning, standard-hardware throughput gains (up to 46%) from physically reduced matrix dimensions, and compression feasibility on a single GPU within hours. A fourth strength — matching gradient-based methods in zero-shot retention — is reported but model-family-specific.
Is MoDeGPT a good no-gradient structured method? For Llama-2/3 and OPT at 25–30% compression, the paper's evidence supports yes. For other architectures, MoE models, or compression ratios above 30%, the method is promising but unvalidated in the published benchmarks.
Bottom Line: MoDeGPT's core contribution is demonstrating that module-level output reconstruction is a sufficient proxy for task loss to eliminate recovery fine-tuning from the structured LLM compression pipeline — at least for standard decoder Transformers at moderate compression rates. The method is not a silver bullet for all compression scenarios, and the reported gains are tightly scoped to Llama-2/3 and OPT at 25–30% compression. Practitioners should treat the paper's numbers as a strong empirical baseline for this specific regime, not a universal performance guarantee.
Sources & References
Production Note: The primary claims in this article derive from the MoDeGPT arXiv abstract and HTML record, and the OpenReview forum record. The exact per-module assignment of Nyström, CR decomposition, and SVD requires the full paper PDF for verification. Before publishing formula-level implementation details or citing specific benchmark tables beyond what appears in the abstract, cross-reference against the full paper text at the arXiv HTML link. No executive or official blog corroboration for 2026-specific deployment claims was verified in this article.
- MoDeGPT: Modular Decomposition for Large Language Model Compression (arXiv:2408.09632) — Primary source; arXiv abstract with all headline benchmark claims
- MoDeGPT full HTML paper (arXiv:2408.09632v1) — Method section with module grouping and decomposition formulation
- MoDeGPT OpenReview record (ICLR 2025) — Peer review record with comparison to ShortGPT, SLEB, and structured compression taxonomy
Keywords: MoDeGPT, Llama-2, Llama-3, OPT, SliceGPT, SparseGPT, SVD, Nyström approximation, CR decomposition, LoRA, Structured pruning, low-rank decomposition, OpenReview, arXiv 2408.09632, ICLR 2025
