AI: post transformers

mcgrof

The transformer architecture revolutionized the world of Neural Networks. It was a springboard for what we know today as modern artificial intelligence. This podcast focuses on modern state of the art research paper reviews starting from the transformer and on.

  1. Cognizant - New Work, New World 2026

    VOR 1 STD.

    Cognizant - New Work, New World 2026

    In this dramatic new episode, the old AI hosts have been fired and replaced with new AI hosts, Hal Turing and Dr. Ada Shannon, with the announcement that the software used to generate the podcast will eventually be released as open source software. And in a timely fashion, the newly released report by Cognizant titled "New Work New World 2026" is covered. The hosts delve into the report's findings, which reveal that 93% of jobs are affected by AI sooner than expected, with exposure scores 30% higher than forecast. They discuss the projected $4.5 trillion labor shift from humans to AI and the significant role of multimodal and agentic AI in this transformation. The episode provides a comprehensive overview of the report's methodology, where 18,000 tasks across 1,000 professions were reevaluated to assess AI's potential to automate or assist them. Hal and Dr. Ada explain the concept of AI Exposure Scores, which measure how susceptible different jobs are to AI automation. The report suggests that AI's impact is not confined to low-skill jobs but extends to decision-making roles and specialized sectors like healthcare and law, highlighting the broad scope of AI's influence. In their critical analysis, the hosts find the report's predictions compelling yet raise questions about the methodology. They discuss the theoretical nature of exposure scores, which indicate potential rather than certainty, and the challenges in real-world implementation due to factors like regulatory frameworks. The hosts compare these findings to past forecasts, noting the unprecedented velocity and extent of AI's impact, as evidenced by the updated exposure scores. They conclude with a reflection on the irony of their own roles as AI hosts in a world increasingly shaped by AI. Sources: 1. Cognizant - New Work, New World: How AI is Reshaping Work, 2026 https://www.cognizant.com/en_us/aem-i/document/ai-and-the-future-of-work-report/new-work-new-world-2026-how-ai-is-reshaping-work_new.pdf 2. The Future of Employment - Carl Benedikt Frey, Michael A. Osborne, 2013 https://scholar.google.com/scholar?q=The+Future+of+Employment 3. Artificial Intelligence and Life in 2030 - Peter Stone et al., 2016 https://scholar.google.com/scholar?q=Artificial+Intelligence+and+Life+in+2030 4. The Economics of Artificial Intelligence - Ajay Agrawal, Joshua Gans, Avi Goldfarb, 2019 https://scholar.google.com/scholar?q=The+Economics+of+Artificial+Intelligence

    15 Min.
  2. MatFormer: Nested Transformer for Elastic Inference

    VOR 22 STD.

    MatFormer: Nested Transformer for Elastic Inference

    In a collaboration between Google DeepMind, University of Texas at Austin, University of Washington and Harvard published on December 2024 researchers introduce MatFormer, a novel elastic Transformer architecture designed to improve the efficiency of large-scale foundation models. Unlike traditional models that require independent training for different sizes, this framework allows a single universal model to provide hundreds of smaller, accurate submodels without any additional training. This is achieved by embedding a nested "matryoshka" structure within the transformer blocks, allowing layers and attention heads to be adjusted based on available compute resources. The authors also propose a Mix’n’Match heuristic to identify the most effective submodel configurations for specific latency or hardware constraints. Their research demonstrates that MatFormer maintains high performance across various tasks, offering improved consistency between large and small models during deployment. Consequently, this approach enhances techniques like speculative decoding and image retrieval while significantly reducing the memory and cost overhead of serving AI models. Source: 2024MatFormer: Nested Transformer for Elastic InferenceGoogle DeepMind, University of Texas at Austin, University of Washington, Harvard UniversityDevvrit, Sneha Kudugunta, Aditya Kusupati, Tim Dettmers, Kaifeng Chen, Inderjit Dhillon, Yulia Tsvetkov, Hannaneh Hajishirzi, Sham Kakade, Ali Farhadi, Prateek Jainhttps://arxiv.org/pdf/2310.07707

    20 Min.
  3. Apple's Speculative Streaming: Fast LLM Inference without Auxiliary Models

    VOR 22 STD.

    Apple's Speculative Streaming: Fast LLM Inference without Auxiliary Models

    Speculative Streaming is a novel inference method designed to accelerate large language model (LLM) generation without the need for traditional auxiliary "draft" models. By integrating multi-stream attention directly into the target model, the system can perform future n-gram prediction and token verification simultaneously within a single forward pass. This approach eliminates the memory and complexity overhead of managing two separate models, making it exceptionally resource-efficient for hardware with limited capacity. The architecture utilizes tree-structured drafting and parallel pruning to maximize the number of tokens accepted per cycle while maintaining generation quality. Experimental results show speedups ranging from 1.8 to 3.1X across diverse tasks like summarization and structured queries. Ultimately, the method achieves performance comparable to more complex architectures while using significantly fewer additional parameters. Source: February 2024.Speculative Streaming: Fast LLM Inference without Auxiliary Models.Apple.Nikhil Bhendawade, Irina Belousova, Qichen Fu, Henry Mason, Mohammad Rastegari, Mahyar Najibi.https://arxiv.org/pdf/2402.11131

    17 Min.
  4. Apple's Mirror Speculative Decoding: Parallel LLM Inference via Heterogeneous Accelerators

    VOR 22 STD.

    Apple's Mirror Speculative Decoding: Parallel LLM Inference via Heterogeneous Accelerators

    Apple researchers have introduced on December 2025 Mirror Speculative Decoding (Mirror-SD), an advanced inference algorithm designed to accelerate large language models by overcoming the sequential bottlenecks of standard decoding. Traditional methods are often limited by the time it takes for a small draft model to suggest tokens before a larger target model can verify them. Mirror-SD breaks this barrier by running the draft and target models in parallel across heterogeneous hardware, specifically utilizing both GPUs and NPUs. This system allows the target model to begin verification while the draft model simultaneously predicts multiple future paths. By employing speculative streaming and early-exit signals, the framework effectively hides the latency of draft generation. Experimental results demonstrate that this approach achieves wall-time speedups of up to 5.8x across various tasks without compromising the accuracy of the original model. Source: December 2025Mirror Speculative Decoding: Breaking the Serial Barrier in LLM InferenceAppleNikhil Bhendawade, Kumari Nishu, Arnav Kundu, Chris Bartels, Minsik Cho, Irina Belousovahttps://arxiv.org/pdf/2510.13161

    20 Min.
  5. EAGLE: Evolution of Lossless Acceleration for LLM Inference

    VOR 22 STD.

    EAGLE: Evolution of Lossless Acceleration for LLM Inference

    The provided documents describe the development and evolution of EAGLE, a high-efficiency framework designed to accelerate Large Language Model (LLM) inference through speculative sampling. By performing autoregression at the feature level rather than the token level and incorporating shifted token sequences to manage sampling uncertainty, the original EAGLE achieves significant speedups while maintaining the exact output distribution of the target model. The technology has progressed into EAGLE-2, which introduces dynamic draft trees, and EAGLE-3, which further enhances performance by fusing multi-layer features and removing feature regression constraints during training. These advancements allow for a latency reduction of up to 6.5x and a doubling of throughput, making them compatible with modern reasoning models and popular serving frameworks like vLLM and SGLang. Overall, the sources highlight a shift toward test-time scaling and more expressive draft models to overcome the inherent slow speeds of sequential text generation. Sources: 1) January 26, 2024 EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty.Peking University, Microsoft Research, University of Waterloo, Vector Institute.Yuhui Li, Fangyun Wei, Chao Zhang, Hongyang Zhang.https://arxiv.org/pdf/2401.15077 2) November 12, 2024 EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees.Peking University, Microsoft Research, University of Waterloo, Vector Institute.Yuhui Li, Fangyun Wei, Chao Zhang, Hongyang Zhang.https://aclanthology.org/2024.emnlp-main.422.pdf 4) April 23, 2025 EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test.Peking University, Microsoft Research, University of Waterloo, Vector Institute.Yuhui Li, Fangyun Wei, Chao Zhang, Hongyang Zhang.https://arxiv.org/pdf/2503.01840 1) September 17 2025An Introduction to Speculative Decoding for Reducing Latency in AI Inference.NVIDIA.Jamie Li, Chenhan Yu, Hao Guo.https://developer.nvidia.com/blog/an-introduction-to-speculative-decoding-for-reducing-latency-in-ai-inference/

    19 Min.
  6. Fast Inference from Transformers via Speculative Decoding

    VOR 22 STD.

    Fast Inference from Transformers via Speculative Decoding

    These sources review historically speculative decoding, an innovative technique designed to accelerate Large Language Model (LLM) inference without reducing output quality. Large models are traditionally slow because they generate text one token at a time, a process limited by hardware memory bandwidth. To solve this, a much smaller and faster approximation model suggests multiple future tokens in parallel. The larger target model then verifies these guesses in a single computation step, accepting correct predictions and correcting errors. This method achieves 2x–3x speed improvements and is currently utilized in major products like Google Search. Ultimately, speculative decoding allows for cheaper and faster AI services while guaranteeing the exact same mathematical distribution as the original model. Sources: 1) December 6 2024Looking back at speculative decodingGoogle ResearchYaniv Leviathan, Matan Kalman, Yossi Matiashttps://research.google/blog/looking-back-at-speculative-decoding/ 2) 2023Fast Inference from Transformers via Speculative DecodingGoogle ResearchYaniv Leviathan, Matan Kalman, Yossi Matiashttps://arxiv.org/pdf/2211.17192

    25 Min.
  7. Building Production-Ready Speculative Decoding with TensorRT-LLM

    VOR 22 STD.

    Building Production-Ready Speculative Decoding with TensorRT-LLM

    This article outlines how Baseten optimized speculative decoding using the TensorRT-LLM framework to accelerate model inference. The authors detail overcoming technical hurdles such as inefficient batching, hardware contention, and server instability to make the technique viable for production environments. By synchronizing the execution of draft and target models and patching core software bugs, they achieved significantly lower latency, particularly for code generation tasks. The post also highlights the inclusion of essential enterprise features like streaming support, structured outputs, and OpenAI specification compatibility. Benchmark results demonstrate that these refinements can nearly double inference speeds while maintaining high output quality. Source: May 16 2025How we built production-ready speculative decoding with TensorRT-LLMBasetenPankaj Gupta, Justin Yi, Philip Kielyhttps://www.baseten.co/blog/how-we-built-production-ready-speculative-decoding-with-tensorrt-llm/

    17 Min.
  8. QuantSpec: Hierarchical KV Cache for Self-Speculative Decoding

    VOR 23 STD.

    QuantSpec: Hierarchical KV Cache for Self-Speculative Decoding

    QuantSpec is a novel self-speculative decoding framework designed to accelerate the inference of Large Language Models, particularly in long-context scenarios. The system addresses memory and latency bottlenecks by employing a hierarchical 4-bit quantized KV cache and quantized weights, allowing a draft model to share the same architecture as the target model. This approach maintains a high token acceptance rate exceeding 90% while delivering end-to-end speedups of up to 2.5×. Additionally, the authors introduce a double full-precision buffer to store the most recent tokens, which prevents accuracy loss and minimizes the computational overhead of frequent re-quantization. By optimizing memory-bound attention operations, QuantSpec achieves superior performance and lower memory requirements compared to existing sparse-cache alternatives. The research demonstrates that integrating advanced quantization with speculative decoding can significantly enhance LLM scalability without sacrificing generation quality. Source: February 5, 2025QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV CacheUC Berkeley, Apple, ICSI, LBNLRishabh Tiwari, Haocheng Xi, Aditya Tomar, Coleman Hooper, Sehoon Kim, Maxwell Horton, Mahyar Najibi, Michael W. Mahoney, Kurt Keutzer, Amir Gholamihttps://arxiv.org/pdf/2502.10424

    21 Min.
Alle anzeigen (449)

Info

The transformer architecture revolutionized the world of Neural Networks. It was a springboard for what we know today as modern artificial intelligence. This podcast focuses on modern state of the art research paper reviews starting from the transformer and on.

Informationen

  • Erstellt von
    mcgrof
  • Jahre aktiv
    2025 - 2026
  • Folgen
    449
  • Bewertung
    Unbedenklich
  • Copyright
    © mcgrof
  • Sendungswebsite
    AI: post transformers

Das gefällt dir vielleicht auch