“Experiments on a real-world image classification dataset demonstrate that EGT achieves up to 98.97% overall accuracy (matching baseline performance) with a 1.97x inference speedup through early exits, while improving attention consistency by up to 18.5% compared to baseline models.”

“It doesn't just retrieve chunks; it compresses relevant information into "Memory Tokens" in the latent space.”

“the ik_llama.cpp project (a performance-optimized fork of llama.cpp) achieved a breakthrough in local LLM inference for multi-GPU configurations, delivering a massive performance leap — not just a marginal gain, but a 3x to 4x speed improvement.”

“Our estimator can be trained without computing the autocovariance kernels and it can be parallelized to provide the estimates much faster than existing approaches.”

“By reducing propagation steps in LLM deployments, MetaJuLS contributes to Green AI by directly reducing inference carbon footprint.”

“The method achieves approximately $4\sim10 imes$ and $2 imes$ speedups while using $1000$ cores, respectively, under the same level of structural and thermodynamic accuracy and with a reduced memory usage.”

“HiGR delivers consistent improvements in both offline evaluations and online deployment. Specifically, it outperforms state-of-the-art methods by over 10% in offline recommendation quality with a 5x inference speedup, while further achieving a 1.22% and 1.73% increase in Average Watch Time and Average Video Views in online A/B tests.”

“FlowBlending achieves up to 1.65x faster inference with 57.35% fewer FLOPs, while maintaining the visual fidelity, temporal coherence, and semantic alignment of the large models.”

“Utilizing 2:4 sparsity combined with quantization on $4096 imes 4096$ matrices, our approach achieves a reduction of up to $4\times$ in weight storage and a $1.71\times$ speedup in matrix multiplication, yielding a $1.29\times$ end-to-end latency reduction compared to dense GPU baselines.”

“RGTN achieves state-of-the-art compression ratios and runs 4-600$\times$ faster than existing methods.”

“Experiments demonstrate that Youtu-Agent achieves state-of-the-art performance on WebWalkerQA (71.47%) and GAIA (72.8%) using open-weight models.”

“CorGi and CorGi+ achieve up to 2.0x speedup on average, while preserving high generation quality.”

“RainFusion2.0 can achieve 80% sparsity while achieving an end-to-end speedup of 1.5~1.8x without compromising video quality.”

“PipeFlow achieves up to a 9.6X speedup compared to TokenFlow and a 31.7X speedup over Diffusion Motion Transfer (DMT).”

“The surrogate is orders of magnitude faster than SOLPS-ITER, enabling rapid parameter exploration.”

“Yggdrasil achieves up to $3.98\times$ speedup over state-of-the-art baselines.”

“The algorithm consistently achieves lower error than the fluid-based method, and even one order of magnitude lower in a fusion-relevant test case. Moreover, the algorithm exhibits a significant speedup compared to the reference kinetic MC method.”

“AKG kernel agent achieves an average speedup of 1.46x over PyTorch Eager baselines implementations.”

“INT8 quantization consistently preserves over 90% of the FP16 baseline accuracy and achieves a 1.5x prefill speedup on the Atlas A2.”

“The main findings is that when running certain models partially offloaded to GPU, some models perform much better on Vulkan than CUDA”

“LogosQ leverages Rust static analysis to eliminate entire classes of runtime errors, particularly in parameter-shift rule gradient computations for variational algorithms.”

“Prompt Choreography significantly reduces per-message latency (2.0--6.2$ imes$ faster time-to-first-token) and achieves substantial end-to-end speedups ($>$2.2$ imes$) in some workflows dominated by redundant computation.”

“Implementation of AETHER-X: Adaptive POVM Kernels for 4.9x Inference Speedup.”

“WeDLM preserves the quality of strong AR backbones while delivering substantial speedups, approaching 3x on challenging reasoning benchmarks and up to 10x in low-entropy generation regimes; critically, our comparisons are against AR baselines served by vLLM under matched deployment settings, demonstrating that diffusion-style decoding can outperform an optimized AR engine in practice.”

“FoldAct explicitly addresses challenges through three key innovations: separated loss computation, full context consistency loss, and selective segment training.”

“The caching strategy is model-agnostic and can be applied to other off-the-shelf multi-view networks without retraining.”

“MERIT achieves a geometric mean speedup of 10.9% with peak improvements of 32x compared to hardware branch predictor.”

“FUSCO achieves up to 3.84x and 2.01x speedups over NCCL and DeepEP (the state-of-the-art MoE communication library), respectively.”

“Our comparative analysis shows 1-bit models achieve 5.07 times speedup over 2-bit alternatives on ARM processors while maintaining 92% task accuracy.”

“ADT-Tree achieves speedups of 3.13x and 3.05x, respectively, on MS-COCO 2017 and PartiPrompts.”

“LIME achieves 1.7x and 3.7x speedups over state-of-the-art baselines under sporadic and bursty request patterns respectively, without compromising model accuracy.”

“QFWP achieves lower RMSE and higher directional accuracy at all batch sizes, while QLSTM reaches the highest throughput at batch size 64, revealing a clear speed accuracy Pareto frontier.”

“The method is linear by construction, each time step requires only one linear solve. Across the benchmark suite, this reduces wall-clock time by $2$--$4\times$ relative to fully implicit nonlinear formulations while maintaining comparable accuracy.”

“How many of you used --fit flag on your llama.cpp commands? Please share your stats on this(Would be nice to see before & after results).”

“ST-MoE outperforms state-of-art in accuracy but also reduces model parameter by 41.38% and achieves a 3.6x speedup in training.”

“A Fixed-Volume Variant of Gibbs-Ensemble Monte Carlo yields Significant Speedup in Binodal Calculation.”

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“AutoJudge accelerates LLM inference by identifying which token mismatches actually matter.”

“EXO 1.0 accelerates LLM inference 4x.”

“LLM inference that gets faster as you use it. Our runtime-learning accelerator adapts continuously to your workload, delivering 500 TPS on DeepSeek-V3.1, a 4x speedup over baseline performance without manual tuning.”

“(Assuming a quote about speed or efficiency) "Achieving 50x speedup unlocks new possibilities for on-device AI."”

“Rust: ~8000 embeddings/sec (~1.7× speedup)”

“The article doesn't contain a direct quote, but the core claim is the 50x speedup.”

“Zhipu.AI open-sources faster GLM models (8x speedup), launches Z.ai, aiming for global expansion, potentially ahead of IPO.”

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“The article claims a 26x speedup in inference with a novel Layer-Condensed KV Cache.”

“Further details on the specific implementation and performance metrics would be needed to fully assess the impact of this technique.”

“Further details on the specific techniques used, such as quantization methods or hardware optimizations, would be valuable.”

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